Analysis of National High-level Talent Evaluation Standards and the Application of the DIKWP Model
Yucong Duan
International Standardization Committee of Networked DIKWPfor Artificial Intelligence Evaluation(DIKWP-SC)
World Academy for Artificial Consciousness(WAAC)
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email: duanyucong@hotmail.com)
Introduction
High-level talent is the key force leading technological innovation and industrial upgrading. Governments at all levels have formulated high-level talent evaluation standards to identify and attract outstanding talents in various fields. These standards usually evaluate talents' academic achievements, professional skills, innovative achievements, and social contributions through a classification and grading system, in order to provide corresponding policy support and incentives. In recent years, Chinese provinces and cities have developed commonalities as well as distinctive practices in their high-level talent evaluation systems. For example, top scientists and industry leaders are generally listed as the highest level, but some places emphasize traditional honorary titles in specific indicators, while others introduce market-oriented evaluation indicators. Facing the rapid development of future industries such as Artificial Intelligence (AI) and Artificial Consciousness (AC), existing talent evaluation standards need to be further improved to achieve the optimal matching and dynamic evaluation of talent capabilities with corporate job requirements. To this end, we will conduct an in-depth analysis and summary of high-level talent evaluation standards across the country, and introduce the DIKWP model (Data-Information-Knowledge-Wisdom-Purpose model). Using the method of migrating from conceptual space to semantic space, we will conduct a mapping analysis of these standards in the 25 modules of the DIKWP × DIKWP semantic space, comparing the key DIKWP interaction paths emphasized by different standards. On this basis, facing the future industrial development of AI and artificial consciousness, we will design an optimized talent evaluation and development mechanism, and illustrate how to achieve dynamic matching of talent capability evaluation and job requirements through simulation cases.
Overview of National High-level Talent Evaluation Standards
China's high-level talent evaluation typically adopts a classification and grading system, identifying qualified talents into different levels to provide corresponding support and treatment. For example, many regions divide high-level talents into several categories such as A, B, C... (or tiers), with each category corresponding to different achievement requirements and policy benefits. Usually, the highest level (such as Class A or the first tier) includes internationally top talents, such as winners of major awards like the Nobel Prize, Turing Award, Fields Medal, etc., and academicians of the two academies. The next-highest level covers recipients of major national talent plans (such as the central "Thousand Talents Plan," "Ten Thousand Talents Plan," etc.), and leading talents in key national fields. The lower levels are formulated according to different fields, such as provincial and ministerial award winners, top industry experts, senior executives of well-known enterprises, and even young top-notch talents. The following are the common characteristics of high-level talent tiering:
·Top Tier: Talents who have received the highest international awards or honors, such as major international award winners, academicians, National Highest Science and Technology Award winners, etc. This type of talent is regarded as a strategic scarce resource, and all regions will give them the highest level of support.
·National Level: Talents selected for major national talent projects or with equivalent national leadership status, such as top talents and teams of the national "Thousand Talents Plan," recipients of the National "Ten Thousand Talents Plan," recipients of the Chinese Academy of Sciences' "Hundred Talents Program," etc. They usually have outstanding influence nationwide.
·Provincial/Ministerial/Industry Level: Talents who have made outstanding achievements at the provincial, ministerial, or industry level, such as provincial or ministerial science and technology award winners, experts enjoying special government allowances from the State Council, industry masters, master teachers, senior executives of well-known enterprises, etc. These talents are leaders in their region or industry.
·Local and Youth Level: Includes those with senior professional technical titles, highly educated talents such as PhDs, or young talents who have made outstanding contributions locally and have development potential. This level focuses on reserving and cultivating future high-level talents.
It is worth noting that different regions have differences in specific evaluation indicators. Some regions traditionally favor "hats"—that is, the titles and honors obtained by the talent, such as academician, recipient of a certain plan, etc., using them as the main basis for entering the corresponding level. However, over-reliance on talent titles can easily lead to the phenomenon of "judging solely by hats," ignoring the actual ability and contribution of the talent. Therefore, the state has advocated in recent years to break the "hat-only" tendency and pay more attention to the actual performance and value creation of talents. At the same time, some regions have begun to introduce market-oriented evaluation ideas, using objective data such as salary level and tax contribution as measure indicators of talent value. The background for this practice is that the market can, to a certain extent, reflect the scarcity and contribution value of talents. For example, a high salary often means that the talent is highly recognized in the market.
Overall, the talent evaluation standards of provinces and cities across the country are similar in level setting, but have different emphases on evaluation dimensions. In the following, we will focus on several representative regions (such as Hainan Free Trade Port, Beijing, Shanghai, Shenzhen in Guangdong, Zhejiang, etc.) to introduce the characteristics of their high-level talent evaluation standards, and extract commonalities and differences from them.
Analysis of High-level Talent Evaluation Standards in Typical Regions
Hainan Free Trade Port High-level Talent Classification Standards
As a free trade port with Chinese characteristics, Hainan has boldly innovated in its talent policy. Since 2020, the Hainan Free Trade Port has implemented new High-level Talent Classification Standards, dividing high-level talents into five categories: A, B, C, D, and E. The evaluation system places more emphasis on market orientation. The main characteristics of Hainan's standards include:
·Market Recognition Standards: Core market indicators such as salary and tax payment. For example, it clearly stipulates the personal income tax payment amount or the total tax payment of the enterprise where the talent works that talents in each category need to achieve, to measure the market value of the talent. For example, Class A talents may be required to reach a specific high amount of personal annual salary and tax payment, while Class B is slightly lower, and so on. This practice aims to implement the requirement of "using salary level as the main indicator to evaluate human resource categories" in the Overall Plan for the Construction of Hainan Free Trade Port.
·Professional and Social Recognition Standards: For fields where salary levels are insufficient to reflect the value of talent (such as education and scientific research, healthcare, culture, etc.), professional evaluation indicators are established. These indicators include professional achievements, titles, honorary awards, scientific research results, etc. In the Hainan standards, the specific conditions for talents in each category are stipulated for different industries and fields (such as tropical agriculture, tourism, internet, big data, telecommunications, healthcare, finance, logistics, energy, education and scientific research, culture and sports, etc.). For example, in the field of education and scientific research, Class A may require having served as a leader of a top international scientific research project or having obtained a top domestic scientific research award; in the tourism industry, it may require serving as a senior executive in an internationally renowned enterprise with significant performance.
·PrincOrle of Integrity and Ability: The Hainan standard emphasizes the moral character and social responsibility of talents, while also requiring outstanding performance and contributions. This is consistent with the mainstream value orientation of national talent evaluation, which looks not only at ability but also at moral character and practical contribution.
·Breaking the "Hat-Only" Tendency: Hainan clearly states that existing talent plan titles are not a necessary and sufficient condition for evaluating high-level talents. The popular practice in the past was that if a person was selected for a national-level talent plan, the local government would directly regard them as a high-level talent when identifying them. Hainan, however, weakens this "one hat determines all," paying more attention to the real performance of the talent. For example, for talent project titles of the introduced type, Hainan no longer retains the "hat" as an evaluation requirement, but looks at the actual achievements behind it.
·Internationalization and New Business Formats: As a free trade port, Hainan particularly highlights the evaluation of international talents, incorporating internationally accepted talent standards as much as possible. For example, it recognizes international professional qualifications, international awards, etc. At the same time, Hainan also evaluates talents in new business formats without sticking to one pattern, including talents in emerging fields urgently needed for the construction of the free trade port. For example, e-sports champions, web anchors, and well-known online writers are also included in the scope of high-level talent evaluation. This breaks the limitation of traditional talent evaluation that only focuses on academic and technical fields, and reflects the idea of updating standards in sync with industrial development.
The Hainan High-level Talent Identification Measures have also been issued accordingly, clarifying the identification process and the delegation of authority. For example, the identification authority for Class E and Class D talents is delegated to cities, counties, or parks to improve efficiency. Hainan's evaluation system, which is oriented by market value and practical contribution, and takes into account the characteristics of various fields, is considered more scientific, standardized, and precise. It highlights full-domain coverage, values both market competitiveness and the characteristics of the public domain, and provides a demonstration for the reform of national talent evaluation.
Talent Evaluation Standards in Beijing and Other First-tier Cities
As the capital, Beijing's talent policy is exemplary. The talent evaluation standards at the Beijing municipal level include multiple plans and measures. For example, Beijing has launched "Measures for the Assessment and Identification of Senior Professional Titles for High-level, Urgently Needed, and Special Talents," etc., to open up green channels in professional title evaluation to assess high-level and urgently needed talents. At the same time, various districts have also formulated identification standards that fit their own positioning.
Taking Tongzhou District, Beijing as an example, its high-level talent identification standards divide talents into six levels, basically covering all types of situations from top international talents to outstanding skilled talents. Specifically:
·First Level: Winners of top international awards and honors. Such as Nobel Prize, Turing Award, Fields Medal winners, academicians of the two academies, National Highest Science and Technology Award winners, and academicians of academies of sciences in developed countries such as the US, UK, Canada, Australia, etc.
·Second Level: Recipients of major national talent projects and talents of equivalent level. Such as top talents and teams of the central "Thousand Talents Plan," recipients of the "Ten Thousand Talents Plan," recipients of the Chinese Academy of Sciences' "Hundred Talents Program," etc.
·Third Level: Winners of national-level professional awards and senior executives of large enterprises, etc. Such as national-level teaching masters, winners of the China Grand Skill Award; middle-level managers of Fortune 500 companies or senior managers of domestic Top 100 companies with more than 3 years of service, etc.
·Fourth Level: Provincial and ministerial-level experts and outstanding talents in this region. Such as experts enjoying special government allowances from the State Council; and winners of outstanding talents selected by Tongzhou District of Beijing, Wuqing District of Tianjin, and Langfang City of Hebei, etc.
·Fifth Level: Senior professional talents and enterprise backbones. Such as those with senior professional titles; middle-level managers of domestic Top 100 companies with more than 3 years of service, etc.
·Sixth Level: Young highly educated and skilled talents. Such as provincial-level master teachers, academic leaders; provincial technical experts, senior technicians; PhD degree holders; and other talents with special skills and outstanding performance, etc.
It can be seen that the Tongzhou standard comprehensively considers multiple dimensions of achievements in scientific research (awards, titles), industry (enterprise work experience), and education/skills (teachers, technicians), and forms a linkage with the talent evaluation in surrounding areas of Beijing-Tianjin-HeHebei (mutual recognition of each other's outstanding talent titles). Other districts in Beijing and even the talent plans at the Beijing municipal level (such as the Beijing "Phoenix Program," etc.) also generally follow this line of thought, with only slight differences in names and details. Overall, the evaluation systems of first-tier cities such as Beijing and Shanghai will place more emphasis on the level and influence of talents, and attract talents through resources such as household registration, housing, and subsidies. For example, in Tongzhou's talent apartment policy, talents at different levels can enjoy rent reductions or purchase subsidies for corresponding areas, and the highest-level talents can live in 100-square-meter apartments for free.
Shanghai's Talent Classification and Incentive Mechanism
As an international metropolis, Shanghai's classification and identification of high-level talents are equally meticulous, and it pays attention to the introduction of overseas talents. Shanghai generally divides talents into four grades: A, B, C, and D, where A is the top and D is the lower. For example, in the "Shanghai Overseas High-level Talent Residence Permit" system, overseas talents are divided into A and B categories according to conditions. Class A belongs to foreign high-end talents, and Class B is for urgently needed professional talents, enjoying different entry-exit and residence conveniences. In terms of local talents, Shanghai's district-level talent plans (such as Minhang District's "Chunshen Pyramid Talent Program") even subdivide talents into four levels and three categories: the four levels include "Excellent, Outstanding, Elite, Young Innovation," and the three categories refer to "Academic, Technical, and Management." This classification reflects the classified support for different development stages (senior vs. young) and different role types (scientific research, technical application, management).
Shanghai also pays great attention to linking talent incentive measures with classification. For example, for newly introduced high-level talents, housing purchase subsidies of up to 5 million, 3 million, and 1 million yuan, or rent subsidies of corresponding amounts, are given to A, B, and C categories, respectively. Another example, Shanghai's Pudong, Baoshan, and other districts have formulated high-level talent classification standards, and have separately detailed the standards for A/B talents in systems such as science and technology, health, etc. These measures all reflect that Shanghai, in talent evaluation, not only values the scientific rigor of the classification standards themselves, but also pays more attention to the attractiveness of supporting policies.
Talent Evaluation Characteristics in Guangdong Province and Shenzhen
As a major economic province, Guangdong's evaluation of high-level talents values both market performance and innovation/entrepreneurship. As early as the 2010s, Shenzhen launched the High-level Professional Talent identification measures, dividing talents into four categories: Outstanding Talents, National-level Leading Talents, Local-level Leading Talents, and Reserve Talents, as well as A, B, and C three-category confirmation measures for overseas talents. The unique features of Shenzhen's classification are:
·Local Classification vs. Overseas Classification: Shenzhen has four grades for local high-level professional talents, and lists three grades A/B/C separately for overseas introduced talents, introducing them through different channels.
·Rewards and Subsidies: Shenzhen provides high-level talents with nationally leading living subsidies. Outstanding talents can receive a one-time reward of up to 6 million yuan, national-level leading/Class A talents 3 million, local leading/Class B 2 million, and reserve-level/Class C 1.6 million. The high subsidies are among the highest in the country, reflecting Shenzhen's use of real money to attract talents.
·Innovation and Entrepreneurship Orientation: Shenzhen particularly encourages entrepreneurial talents. For example, it gives high-level talent identification to talents who start high-tech enterprises and obtain venture capital. Shenzhen's "Peacock Plan" specifically provides funding for overseas high-level talents to start businesses. This is reflected in the evaluation standards, where talents who have achieved innovative results in the market and brought economic benefits will be given a higher evaluation.
·Age and Development Potential: Shenzhen's talent measures set age limits for talents in various categories (e.g., Class A < 60 years old, Class B < 55, Class C < 40), which can be relaxed for particularly outstanding individuals. This shows a tilt towards young and promising talents.
Overall, the talent evaluation in other cities in Guangdong (such as Guangzhou, Zhuhai) has also mostly introduced economic contribution indicators. For example, in Guangzhou's past talent standards, entrepreneur-type talents were required to have their enterprise operating income or tax payment reach a certain scale. As a region with a developed market economy, Guangdong generally balances traditional honors and market achievements in talent evaluation.
Talent Evaluation Measures in Zhejiang Province
Zhejiang Province, especially cities like Hangzhou and Ningbo, has also innovated in talent evaluation due to its developed private economy and digital economy. Many cities in Zhejiang have implemented talent introduction plans such as the "Kunpeng Plan" and "Honghu Plan," classifying and identifying high-level talents and giving them financial support and housing benefits. For example, Hangzhou divides high-level talents into five categories: A, B, C, D, and E (similar to Hainan), covering Nobel Prize winners, academicians (Class A), recipients of the national Thousand Talents Plan (Class B), recipients of provincial key talent projects (Class C), young talents such as PhDs (Class D/E), etc., and gives them settling-in subsidies of more than 1 million yuan (top talents even get tens of millions in scientific research funds) and supporting services such as children's schooling and spouse placement. Zhejiang also strongly emphasizes entrepreneurs and industrial talents, such as including the main founders of listed companies and founders of high-growth enterprises in the scope of high-level talents, to encourage innovation and entrepreneurship. In terms of evaluation standards, many places in Zhejiang identify talents through a combination of market recognition + expert review, i.e., they not only examine their enterprise performance, tax payment, and other hard indicators, but also have expert committees score their technical level and innovative achievements.
Through the above comparison, we can see the commonalities and differences of high-level talent evaluation standards nationwide:
·Commonalities: All regions have established a hierarchical classification system, and the highest level is invariably aimed at top international and domestic talents; talents in science and technology, education, and industry are generally valued;
the evaluation content covers multiple dimensions such as academic achievements, professional qualifications, honorary awards, innovative achievements, and economic contributions; moral character and social contribution also usually appear as basic conditions.
·Differences: In the choice of indicators, some regions place more emphasis on academia and honors (such as the traditional "hat" orientation), while others place more emphasis on economic and social benefits (such as the salary and tax indicators in Hainan and Shenzhen); some regions have fine-grained classifications (up to six levels), while others are relatively rough (three levels or no specific categories, only defining the scope). In addition, different regions will add special clauses according to their own leading industries and strategic needs, such as Hainan adding new professional talents like e-sports anchors, Shenzhen focusing on entrepreneurial talents, and Shanghai paying attention to financial and shipping talents. These differences reflect the regional customization of talent evaluation: it not only follows the national orientation, but also serves local development needs.
However, no matter what the standard, they all face a common challenge: how to ensure that the evaluation system comprehensively and scientifically reflects the ability and potential of talents, and is adjusted in time with environmental changes? Traditional conceptual classification sometimes makes it difficult to quantitatively compare talents in different fields, and it is also difficult to reflect the growth and progress of talents in a timely manner. In emerging fields such as AI and artificial consciousness, which are developing rapidly, this problem is even more prominent—many outstanding talents may not have traditional titles or long-term accumulation, but have unique contributions in new technologies. Therefore, we introduce the DIKWP model, trying to re-examine and reconstruct the talent evaluation standards from the perspective of cognitive semantics.
Introduction to the DIKWP Model: From Conceptual Space to Semantic Space
The DIKWP model was proposed by Professor Yucong Duan of Hainan University. It is an extension of the classic "Data-Information-Knowledge-Wisdom (DIKW)" model, adding a "Purpose" layer at the end, thus forming a five-level cognitive framework of Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P). The biggest feature of the DIKWP model is that it is not linear and unidirectional, but a networked interactive structure: there are bidirectional feedback and iterative relationships between the five elements, forming a multi-directional flowing semantic network. In short, the DIKWP model simulates the various stages of an intelligent agent's cognitive process: from the acquisition of data, to the processing of information, to the formation of knowledge, then to the generation of wisdom (the ability to solve problems), and finally, wisdom is elevated and condensed into Purpose to guide action. At the same time, the higher-level Purpose will in turn affect the data selection and information processing of the lower levels, thus forming a cognitive closed loop.
In the DIKWP model, each level can be understood as follows:
·Data (D): Raw facts and values, unprocessed input. Corresponding to talent evaluation, it can be analogized to the talent's basic objective indicators, such as the number of papers/patents, sales figures, salary, assessment scores, and other quantifiable data.
·Information (I): Data that has been organized and given meaning. It is the interpretation and presentation of data, with contextual relevance. For example, the awards, professional titles, and industry evaluations received by talents can be seen as transforming raw performance into more meaningful "information." An award itself carries the information about the value of a certain achievement of the talent.
·Knowledge (K): Systematized information, summarized laws, methods, or skills. For an individual, knowledge is embodied as academic qualifications, professional skills, industry experience, etc. High-level talents often have a profound reserve of professional knowledge, which is the foundation of their abilities.
·Wisdom (W): The ability to use knowledge to solve complex problems and create new value, which is a higher-level cognitive performance. A talent's wisdom is manifested in their innovative achievements, strategic decision-making ability, and successful leadership of complex projects. Wisdom means integrating "knowing" and "doing," producing substantial impact.
·Purpose (P): Also known as goal or will, it is the highest-level motivation and goal orientation. It drives the direction of the use of wisdom. For talents, Purpose can be embodied as their mission and vision, sense of social responsibility, career pursuit, and the degree of fit with organizational/national strategic needs. Purpose ensures that the exertion of ability points to a meaningful goal.
From conceptual space to semantic space means transforming the above conceptual levels into a semantic model that can be understood and operated by machines. In traditional talent evaluation standards (conceptual space), we list various conditions (education, awards, titles, performance, etc.) to define a certain type of talent. These are conceptual-level descriptions, which are easy for humans to understand, but are too vague for machines or formal analysis, and there is a lack of internal connections between the various indicators. The DIKWP model provides a semantic space that can represent the various elements of talent evaluation under the five-level cognitive framework, and connect them through clear semantic relationships (such as "information is generated from data," "knowledge breeds wisdom," etc.), thereby forming a structured network.
Furthermore, Professor Yucong Duan's team proposed the DIKWP × DIKWP architecture, which is also vividly called a "dual-loop" cognitive structure. Simply put, this refers to the comprehensive interaction between two cognitive entities or processes that both have the five-layer DIKWP architecture. For example, in an artificial consciousness system, one loop is the basic cognitive flow (the process of D→I→K→W→P), and the other loop is the metacognitive flow (monitoring and regulating the first loop, with corresponding connections between all layers). The two interact with each other, forming the prototype of artificial intelligence's self-awareness. DIKWP × DIKWP produces a 5×5=25-dimensional semantic interaction space. That is to say, if we take the levels of one DIKWP system as rows and the levels of another system as columns, then the combination of any pair of levels represents a kind of information/cognition transformation. There are 25 possible interaction paths in total, and each element can be either the input or the output of the transformation. For example:
·Data → Information (D→I): Processing raw data to extract meaning (this corresponds to the ability of perception and analysis, turning chaotic data into useful information).
·Knowledge → Wisdom (K→W): Applying mastered knowledge to form solutions to problems (corresponding to the innovative ability of comprehensive integration and drawing inferences).
·Wisdom → Data (W→D): High-level wisdom feeds back to the data layer, guiding the collection of new data or producing new original results (corresponding to the fact that experienced people know what additional information should be acquired to verify ideas, which is equivalent to the feedback of wisdom to the front end).
·Purpose → Knowledge (P→K): Purpose-driven, selective acquisition and organization of knowledge (corresponding to learning and accumulating knowledge with a mission, with a clear goal).
·WisD→Purpose (W→P): Elevating wisdom in practice to a higher-level concept or strategy. For example, after completing several projects, a talent summarizes the development direction of the industry and then formulates strategic goals.
The above are just a few examples. In fact, the 25 modules of DIKWP × DIKWP cover the transformation patterns from any one layer to any other layer. Through these interactions, we can meticulously depict a panoramic view of talent capability elements. It is worth emphasizing that these interactions are not all unidirectional; many have bidirectional feedback: Information is not only generated from data, but can also act back on data (such as the selection and filtering of raw data); wisdom not only comes from knowledge, but can also precipitate new knowledge; Purpose not only guides the exertion of wisdom, but can also be revised by bottom-level facts. This network structure gives the DIKWP model the characteristic of dynamic adaptation. In other words, if we use DIKWP to describe the talent evaluation system, then ideally, it should be a system that continuously adjusts based on feedback, rather than a static, one-time evaluation.
DIKWP × DIKWP Semantic Mapping of High-level Talent Evaluation Standards
Now, we will try to map the high-level talent evaluation standards of the various regions introduced earlier into the DIKWP × DIKWP semantic space, analyze which module combinations these standards focus on, and which modules they neglect. Through this mapping, we can reveal the capability dimensions and cognitive paths emphasized by different evaluation systems, so as to compare and evaluate them more intuitively.
Correspondence between Evaluation Standard Elements and DIKWP Levels
First, let's correspond the common talent evaluation elements to the five levels of DIKWP:
·Data Level (D): Corresponds to the quantitative hard indicators in the evaluation standards. For example, personal annual income, tax payment amount, number of papers/patents, project funding, enterprise operating revenue, etc. This data directly reflects the scale of the talent's output in the market or academia. Taking Hainan as an example, the large-scale use of annual salary and tax payment as the basis for classification is a typical data-level indicator.
·Information Level (I): Corresponds to achievements that have been evaluated/recognized. Such as various awards and honors, professional technical titles, talent project selection qualifications, etc. These are all professional or social interpretations of certain data (achievements) of the talent, giving them recognized value. For example, "a provincial science and technology award" contains the information that a certain achievement of the talent has been recognized as being at the provincial leading level. Another example, the title "Professor" indicates that their academic level has reached the senior level, which is an authoritative endorsement of their knowledge and experience.
·Knowledge Level (K): Corresponds to the knowledge and skills mastered by the talent, as well as the experience in forming knowledge. Typical indicators include academic qualifications (PhD, Master's), professional expertise in a research field, years of practice, training experience, etc. These reflect the talent's internal quality and professional accumulation, which are the basis for their subsequent output. For example, a "PhD degree" means systematic higher education training; a "senior engineer title" means rich professional knowledge and experience reserves.
·Wisdom Level (W): Corresponds to the achievements of the talent in using knowledge to create value. This is a higher-level element in the evaluation criteria, including major innovative achievements (inventions, transformation of scientific research results), successful leadership of major projects or teams, solving key industry problems, generating significant economic and social benefits, etc. Many standards try to reflect the wisdom level through performance: such as enterprise executives leading the enterprise to the forefront of the industry, scientists serving as chief experts to solve major national scientific research projects, doctors developing new treatment plans, etc. These examples all show that knowledge has been transformed into practical wisdom results through ability and judgment.
·Purpose Level (P): Corresponds to the talent's motivation, vision, and the degree of docking with external needs. In traditional standards, Purpose is often implicit, but some evaluation indicators can reflect it. For example: whether they serve national strategic fields, whether they are rooted in grassroots service for a long time, and holding important social positions reflects a sense of responsibility. Hainan's inclusion of the key industrial fields of the free trade port in its talent standards is actually a kind of "Purpose matching," that is, only those talents who are aspiring to or have already devoted themselves to the local development priorities are more likely to be identified. At the individual level, Purpose is also reflected in career ideals, such as some plans that particularly encourage overseas talents "aspiring to return to China for development," or talents "determined to devote themselves to the construction of the western region." These orientations are all considerations at the Purpose level.
Through the above correspondence, we have placed the scattered evaluation clauses within the DIKWP framework. From the conceptual space, the conditions listed in the standards of different regions are complicated, but after mapping them to the semantic space, we can see which levels have received more attention. For example, "hat-oriented" standards (like some past practices) mainly fall on the Information level (I) and Knowledge level (K): because titles and awards belong to information, and academic qualifications and professional titles belong to knowledge. If an evaluation system looks almost entirely at these, then it can be said that it defines talents more on the I and K levels. Hainan's market-oriented standards, however, have obviously introduced the Data level (D) and Purpose level (P): salary and tax payment are at the D level, and industry field targeting is at the P level. In this way, it covers a wider DIKWP range, making the talent evaluation more three-dimensional.
Analysis of Standards from the Perspective of DIKWP Interaction Modules
Going a step further, we use the 25 interaction modules of DIKWP × DIKWP for analysis. Here, we need to clarify two systems: the evaluator (such as the government's talent evaluation system) and the evaluated object (the talent themselves). The evaluation standard actually stipulates the process by which the evaluator obtains information from the talent, recognizes their value, and makes a judgment. This can be seen as a cognitive interaction process.
We can regard the evaluation system itself as a DIKWP process, which starts from collecting data and gradually forms a cognition and decision about the talent (identifying a certain type of talent); and regard the talent individual as another DIKWP process, manifested as the talent accumulating knowledge, producing wisdom, and showing Purpose from their own perspective. The interaction between the two occurs between the various levels of the evaluator and the various levels of the talent. For example:
·When the evaluation standard focuses on data indicators, it is actually the evaluation system using its own information processing (I) to read the raw data (D) provided by the talent. For example, reviewing income certificates, number of papers, and other data. This corresponds to the Talent(D) → Evaluator(I) interaction module, i.e., "transforming the talent's data into the evaluator's information."
·When the evaluation committee judges the talent's knowledge and ability through expert review, this is the evaluator using their knowledge and wisdom (K/W layers) to understand the talent's knowledge and wisdom (K/W layers). For example, review experts evaluate their innovation level based on the submitted scientific research results (Talent's W) and elevate it to a judgment of their value (Evaluator forms a P-level decision). This includes a series of module interactions such as Talent(W) → Evaluator(W) and Evaluator(W) → Evaluator(P).
·When a region stipulates that talents in key industrial fields are given priority, it means that the Evaluator's Purpose (P) comes first, corresponding to finding whether the Talent's Wisdom (W) or Knowledge (K) fits this Purpose. That is, the Evaluator(P) → Talent(W/K) interaction: the evaluator selectively pays attention to those talents who have made wisdom contributions in specific fields based on strategic Purpose.
·In the opposite direction, if a talent has a strong will to serve the country (Talent's P) and has made outstanding contributions, their deeds may affect the evaluation system's adjustment of policies or standards (Evaluator's W/P). This can be regarded as the Talent(P) → Evaluator(W/P) interaction. This situation is reflected in reality as some role models causing policymakers to reflect on the current standards, thereby improving the evaluation orientation, which is also a feedback mechanism.
It can be seen that a complete talent evaluation system should make good use of various interaction modules. For example, it should not only look at performance data (D→I), but also observe the process of the talent turning knowledge into wisdom (K→W) through interviews or practical inspections, and also consider whether the talent's ambition is consistent with the organization's needs (W→P, P→W), etc. If certain interactions are lacking, the evaluation may be distorted. Yucong Duan et al. pointed out in artificial intelligence cognitive research that if information transmission is insufficient or asymmetric in the 25 transformation modules of DIKWP, it will lead to cognitive bias. The same is true for talent evaluation: Neglect at any level will lead to an incomplete or even biased understanding of the talent. For example, only looking at income without looking at the original intention of scientific research may miss talents with long-term value but low short-term returns; conversely, only looking at honorary titles without looking at actual contributions may also overestimate "title talents" while ignoring their mediocre performance.
Let's compare the tendencies of the standards of the various regions mentioned above in DIKWP interaction:
·Traditional "Hat" Evaluation (like some past practices) mainly occurs on the Talent(I/K) → Evaluator(I/K) path: the information-level achievements (awards, titles) obtained by the talent are directly accepted by the evaluator and used as a knowledge basis. The link of collecting data from the D layer is weak, and the P layer Purpose inspection is also weak. This can easily lead to information solidification: once identified as a certain type of talent, it remains unchanged for a long time, lacking dynamic feedback.
·Market-oriented Evaluation (like the new standards in Hainan and Shenzhen) strengthens the Talent(D/W) → Evaluator(I/W) path: talents speak with specific performance data and achievements, and the evaluator extracts information and assesses their wisdom value. At the same time, feedback is formed through the Evaluator(P) → Talent(D/W) (such as setting an orientation, guiding talents to strive for certain data or achievements). The advantage of this model is that it is objective and quantifiable, and the feedback is timely; but it is necessary to prevent the risk of over-emphasizing data while ignoring potential quality.
·Comprehensive Evaluation (which many places are trying) strives for full-link coverage: it looks at both the knowledge base and wisdom achievements, both current data and long-term willingness. For example, Beijing, in terms of professional title green cards, innovative talent plans, etc., takes into account academic qualifications, performance, and contribution; Shanghai evaluates academic, technical, and management categories separately; Hainan sets different indicators for different fields. These are all attempts to balance all levels of DIKWP. In terms of interaction, it is manifested as the combined operation of multiple modules, such as (Talent K→Evaluator K) + (Talent W→Evaluator W) + (Evaluator P→Talent W), etc., to portray talents from multiple angles.
Through DIKWP semantic space analysis, we find that an excellent talent evaluation standard should be an organic combination of the 25 modules, rather than a single path. Real-world policies often cannot be perfect in all aspects, but they can play to their strengths and make up for their weaknesses as much as possible. For example, for basic research talents, the market data may not be impressive, but the evaluation system can strengthen the Knowledge→Wisdom (K→W) assessment (look at the originality of their academic thought), to make up for the lack of Data; for business management talents, make more use of the Data→Information (D→I) and Wisdom→Purpose (W→P) assessments (look at performance indicators and strategic vision), while also referring to their professional background information as support.
Overall, the analysis of DIKWP provides us with a holistic view: the standards of different regions are actually choosing different path combinations in the DIKWP network to identify and select talents. And facing emerging fields such as AI and artificial consciousness, we may need an evaluation and cultivation system that covers all key paths to ensure that we do not miss any "shining points" of any kind of outstanding talent. The next section will be based on this to design a talent evaluation and development mechanism for future industries.
Talent Evaluation and Development Mechanism Design for AI and Artificial Consciousness Industries
DIKWP-Oriented Evaluation Mechanism Construction
The Artificial Intelligence (AI) and Artificial Consciousness (AC) industries are highly innovative and complex, requiring talents to span multiple disciplines and skills, and to continuously learn and adapt. Therefore, traditional static talent classification standards find it difficult to timely reflect a talent's growth and value in these new fields. We propose to build a talent evaluation and development mechanism based on the DIKWP model, making full use of its full-link semantic description and feedback characteristics to achieve fine-grained characterization and dynamic updating of talent capabilities.
·Capability Portrait Characterized by DIKWP Modules: Establish a capability portrait for each talent, decomposing their capability elements and mapping them to the 25 interaction modules of DIKWP. This is similar to establishing a multi-dimensional coordinate system for talents: including their performance at the Data layer (e.g., performance indicators), accumulation at the Information layer (e.g., certificates and awards), reserves at the Knowledge layer (professional breadth and depth), creativity and decision-making power at the Wisdom layer, and mission-driven force at the Purpose layer. Furthermore, we look not only at the static values of each layer, but also at the inter-layer transformation capabilities—for example, the "Data→Information" capability represents their ability to extract meaningful patterns from chaotic data (which is crucial for big data AI talents); the "Knowledge→Wisdom" capability represents their ability to apply theory to practice to solve problems (which is critical for AI research transformation); the "Wisdom→Purpose" capability represents their ability to elevate experience into strategic planning (which is important for leadership talents). Through the measurement of these modules, a person's strengths and weaknesses are clear at a glance. For example, an AI engineer's Data Analysis (D→I) and Model Development (K→W) modules have high scores, but their Strategic Awareness (W→P) module is weak, then they are suitable for a technical position but temporarily not suitable for taking the lead in formulating strategies. The evaluation system can plan a development path for them in the technical direction based on this.
·Job Requirements Defined by DIKWP Modules: Similarly, we conduct semantic analysis on corporate jobs, clarifying the key points of demand for each type of job in the DIKWP modules. For example:
oAn AI Algorithm Engineer position may focus on the D→I (extracting information from data) and I→K (formalizing information into models, i.e., knowledge) modules, and also require a certain K→W (applying knowledge to develop high-performance algorithms) module.
oAn Artificial Consciousness Researcher position, however, values the K→W (integrating interdisciplinary knowledge to create innovative theories) and W→P (being able to elevate wisdom to insight into the ultimate purpose of "consciousness") modules, and also requires the ability of P→D (designing experimental data according to research purpose), etc.
oAn AI Product Manager position values I→W (making wise decisions by synthesizing multi-faceted information) and W→P (formulating product vision and strategy), and also requires soft skills such as communication and coordination, which can also be partially mapped to, for example, the I↔W module (switching back and forth between information and wisdom, coordinating needs and technology).
oSenior Managers/Chief Scientists need almost full-link capabilities, but are particularly reflected in the W→P (integrating wisdom achievements into strategy) and P→W (guiding innovation practice with foresight) modules.
By interviewing experts from the employing units and analyzing historical data of the positions, we can establish an "ideal module combination" model for each type of position. In this way, when there is a specific position for recruitment or training, we can clearly know what kind of talent is needed: for example, on which modules they need to reach a high level, and which modules can be cultivated and supplemented later.
·Talent-Job Matching: With the above talent capability portraits and job requirement portraits, precise matching can be performed. The matching algorithm is equivalent to calculating the fit between the talent's DIKWP module vector and the job's requirement vector. Those with a high degree of fit are the better candidates. This is much more refined than screening based only on academic qualifications or years of experience. For example, an artificial intelligence startup is recruiting a "Dialogue System Architect." The key module features they need for the talent may be very strong in D→I (understanding a large amount of user corpus), K→W (designing dialogue algorithms), and W→P (transforming user needs into product functions). If traditional requirements were used, they might only write "PhD in computer-related major, N years of experience." However, through DIKWP matching, they might find a candidate with an undergraduate background but rich practical experience and outstanding contributions in the open-source community. Their capability modules completely match the requirements, and they should also be regarded as an excellent candidate, avoiding missing talents who are very suitable but do not meet traditional conditions.
·Dynamic Evaluation and Feedback: The network characteristics of the DIKWP model allow for continuous monitoring and feedback. Enterprises and talent management departments can regularly update talent portrait data (such as annual performance, newly acquired skills, changes in willingness to grow, etc.), which will be reflected in the changes in DIKWP module values. By comparing the time series of a talent's portrait, one can see their capability curve—that is, their growth path. For example, an AI engineer has significantly improved in the "Knowledge→Wisdom (K→W)" module this year (perhaps because they were independently responsible for a project and successfully solved key problems), and the "Data→Information (D→I)" module has also improved slightly (proficient in using new tools to process data), but the "Wisdom→Purpose (W→P)" is still
very low (insufficient cultivation of strategic awareness). Then the manager can provide targeted training or experience opportunities, such as letting them participate in some product planning discussions, to improve their W→P module. This process forms a closed loop of talent cultivation: evaluation-feedback-re-evaluation every year. The talent's development in different modules is clear at a glance, and the growth direction is clear.
·Optimization of Overall Organizational Effectiveness: By summarizing the talent portraits of all employees, the organization's overall strength in each module can also be analyzed. For example, if an AI company finds that most R&D personnel are weak in the "Purpose layer (P)" module (lacking industry vision), it can introduce external experts or train the team in this area, or focus on supplementing this type of talent in recruitment. Conversely, if the team's technical capabilities are strong (K and W layers are both high), but the ability to analyze and utilize data resources is insufficient (D and I layers are weak), it can consider introducing talents who are good at big data processing or strengthening the construction of data platforms. From the DIKWP perspective, the shortcomings and strengths of the enterprise's talent structure are very intuitive. This is actually similar to the DIKWP evaluation of corporate capabilities, except that the evaluation object here is the team rather than the individual. In practice, some studies have begun to try to use DIKWP modules to evaluate the comprehensive capabilities of enterprises or technical teams and rank them.
Simulation Case: Two-way Matching of Talents and Jobs
To illustrate the above mechanism more concretely, we use two simulation cases to show how two-way matching is achieved:
Case 1: Finding Talents from the Job's Perspective
An R&D company focusing on Artificial Consciousness (AC) plans to recruit an "Artificial Consciousness System Architect." This position requires the candidate to understand both AI technology and have in-depth insights into the theory of human consciousness, and be able to design an AI architecture with preliminary self-cognition functions. Traditional recruitment may find it difficult to directly quantify these requirements, but through DIKWP analysis, the key requirements of the position can be decomposed into:
·At the Knowledge layer (K): Master interdisciplinary knowledge such as cognitive science, neural networks, philosophy, etc.
·At the Wisdom layer (W): Have the ability to synthesize knowledge from different fields to design new architectures and solve unprecedented technical problems.
·At the Purpose layer (P): Have a strong interest and vision for artificial consciousness, and be able to transform abstract concepts into specific system goals (i.e., the W→P module is prominent), and continuously drive the entire R&D process.
Secondly, this position also requires certain data and information layer capabilities, such as reading a large amount of literature (D→I) to draw on all parties' achievements.
The recruitment system will match the portrait of each candidate with the "ideal portrait" above. Suppose there are three candidates:
·A: An AI algorithm expert with rich R&D experience (K→W is high), but has not delved much into consciousness theory (K layer is insufficient in the relevant field), and the driving force of purpose is mainly in the technology itself rather than philosophical exploration (P layer motivation is slightly weak).
·B: Has a PhD in cognitive science, has a sense of mission for artificial consciousness (P layer is strong, K layer is deep in consciousness theory), and has also designed a prototype system (W layer is somewhat embodied), but AI engineering experience is relatively limited (lacks large-scale software architecture practice, D→I, K→W modules are average in the engineering dimension).
·C: A senior architect from a large factory with extremely strong comprehension and system design capabilities (W layer is top-notch), and has also led a team to implement complex projects (W→P is somewhat embodied), but is completely new to the field of artificial consciousness (K layer is almost blank in this field).
Through DIKWP module scoring, the system finds that: Candidate B scores highest on the P→W (using vision to drive innovation) and W→P (refining concepts) modules, and their score on the knowledge layer for consciousness theory is also high, which are exactly what the position values most. Although A has strong hard technical skills, they lack P-layer passion. C has general wisdom but their professional background does not match. So the system recommends B as the best-fit candidate. During the interview process, the company focuses on verifying B's capabilities in the key modules (such as letting them talk freely about their vision for the artificial consciousness architecture = examining W→P, and letting them analyze others' models = examining I→K, K→W). The results confirm the system's judgment. B is successfully hired and quickly becomes competent in the position. This reflects the power of DIKWP matching in the recruitment of high-end composite positions: it breaks through simple resume screening and finds suitable talents from deep semantic matching.
Case 2: Matching Jobs from the Talent's Perspective
A middle-aged and young talent is seeking a position in their career development that can better utilize their talents. We conducted a capability check-up for them through DIKWP, and the portrait results show:
·They are very outstanding in the Data→Information (D→I) module, indicating that they are good at collecting and organizing materials and discovering patterns from data. This may stem from their many years of work in data analysis, where they have trained a strong data sensitivity and pattern recognition ability.
·They are also strong in the Knowledge→Wisdom (K→W) module, which means they can apply the theories they have learned to practice to solve problems. For example, they have overcome several technical difficulties at work, which proves this.
·However, their score on the Wisdom→Purpose (W→P) module is low, indicating that they are not good at formulating strategies from a global perspective, or lack experience in leadership and decision-making. The Information→Knowledge (I→K) module is also average, indicating that they are slightly weak in abstract summarization and theoretical improvement (perhaps their academic background is mediocre and they lack systematic theoretical training).
Overall, they are a pragmatic talent, strong in execution, and weak in strategic planning and forward-looking research. Then the positions suitable for them should be those that require strong execution and application capabilities, while the requirements for cutting-edge theoretical innovation are relatively secondary. For example:
·Data Science Project Manager: This requires them to lead a team to mine the value of data (D→I, K→W capabilities are just right), while the macro strategy of the project is usually formulated by higher levels, and they only need to focus on execution.
·Technical Product Manager (technical-leaning): This requires applying existing technology to products (K→W), and continuously optimizing products based on user data (D→I), and does not necessarily require them to define the product vision (this part can be coordinated with market planning).
·In contrast, "R&D Strategy Planning Positions" or "Frontier Researchers" are not suitable for them, because those positions require strong I→K and W→P.
The system recommends several positions for this talent based on this, and points out the reasons for the match: for example, a company is recruiting a data analysis team leader, requiring proficiency in data pattern mining (D→I) and the ability to guide technology implementation (K→W). These two points are highly consistent with the talent's advantages. Conversely, that position only takes industry foresight as a bonus item, and can tolerate the candidate's shortcomings in W→P. As a result, this talent, after applying, felt like a fish in water, leading the team to turn the company's massive accumulated data into business insights, making up for the company's past shortcoming of "having a data-rich mine but no one good at mining it," and their personal value was fully brought into play.
Through these two simulation cases, we can see that the talent-job matching guided by the DIKWP model is efficient in both directions: when a job is looking for a person, it focuses on the deep capability fit of the person; when a person is choosing a job, they look for an environment where they can exert their strengths and not excessively expose their weaknesses. This two-way matching helps to maximize the coupling of talent value and job contribution.
Dynamic Evaluation and Continuous Development
The field of AI and artificial consciousness is changing with each passing day, and talent evaluation and development must also be a dynamic and evolutionary process. The DIKWP model naturally supports dynamic evaluation: because it regards talents as a continuously circulating cognitive system, their capabilities will continuously change with time and experience, and the evaluation system needs to be updated synchronously to achieve closed-loop management.
·Continuous Data Collection: Using AI technology (such as big data analysis, knowledge graphs), enterprises can continuously collect talents' performance data and autonomous learning data. For example, code contributions, patent output, business indicator completion, training course records, peer evaluations, etc. These fresh Data (D) continuously enter the evaluation system.
·Real-time Information Extraction: Through automatic analysis of the above data, new Information (I) about the talent can be generated in real time: for example, how many high-quality papers they have published this quarter, how much their algorithm performance has improved, and what key Bugs they have solved. This information will update their portrait, allowing managers to grasp the talent's dynamic performance in a timely manner.
·Knowledge Base Update: With the accumulation of evaluation data, the system itself is also enriching its Knowledge (K) of "what kind of capability combination brings success." For example, if it is found that the combination of certain modules is highly correlated with the performance of a certain type of position, this becomes organizable knowledge that can be disseminated, thereby improving the job requirement model or training focus.
·Wisdom-based Decision-making: Based on the latest information and knowledge, managers can make wise decisions (W): such as adjusting talent positions, promoting and using them, rewarding the outstanding, or providing support plans for those with insufficient capabilities. This is similar to the evaluation system itself conducting "wisdom application," continuously optimizing the talent layout and employment strategies.
·Strategic Purpose Calibration: At the highest level, the organization will regularly review the achievement of the Purpose (P) layer: Is the talent team as a whole developing towards the company's strategy? Are there any capabilities that are urgently needed strategically but are in short supply? These will be transformed into new policies and goals, which will feed back to affect the recruitment and training focus of talents in the next stage (i.e., the role of the Evaluator P layer → Talent D/K layer). For example, if the company decides to enter a new technological direction, this is a new strategic Purpose, then the evaluation criteria will add talent measurement indicators in this field accordingly, and actively introduce relevant talents, thus closing the loop from Purpose to data.
In this way, talent evaluation is no longer a static activity of scoring once a year, but becomes talent operation integrated into daily life: it has both immediate state perception and long-term trend analysis. Talents themselves can also benefit from it, because the dynamic evaluation system is equivalent to giving immediate feedback and development suggestions. Everyone can see the changes in their strengths and weaknesses over time through their own portrait, as well as the direction of their next efforts. This is particularly important in the AI field—talents must maintain lifelong learning and rapid iteration, and dynamic evaluation is like a navigator, constantly calibrating the direction of advancement.
Finally, we re-emphasize the special requirements that the development scenario of the AI and artificial consciousness industry puts on talent evaluation, and summarize the advantages of the DIKWP-driven mechanism:
·Cross-domain Integration: AI and AC involve multiple disciplines such as computer science, cognitive psychology, bioneurology, and ethics/philosophy. Traditional single-scale measures cannot measure the value of this type of composite talent. The DIKWP model, through multi-module evaluation, can objectively present the talent's capabilities in different dimensions, so that the advantages of interdisciplinary talents will not be buried. For example, an AI ethics researcher with a background in philosophy may not be good at programming (D layer is average), but their Wisdom→Purpose (W→P) module is strong (can guide AI towards the correct direction); the DIKWP system will recognize their unique value, and thus give them their due position and development space in the talent team.
·Innovation and Uncertainty: The frontier technology field is full of uncertainty, and success is not easy to quantify. The DIKWP evaluation system can supplement the result-oriented evaluation by examining process capabilities (such as the agility of thinking, the path of problem-solving). For example, in a project that has not yet produced output, its future potential can also be evaluated based on the frequent and high-quality interaction of the team at all DIKWP levels (e.g., whether high-level Purpose clearly guides data collection, whether knowledge is continuously precipitated into wisdom). This evaluation is more forward-looking than simply judging heroes by their current achievements.
·Ethics and Responsibility: Technologies such as artificial consciousness bring ethical challenges, so the examination of talents at the Purpose layer is particularly important—do they have the correct values and sense of responsibility to guide technology for good? The DIKWP model explicitly includes "Purpose" in the framework, and can design evaluation indicators in this regard in a targeted manner (such as participation in ethical training, records of adhering to moral principles in product decisions, etc.). In this way, it can not only select talents with correct values, but also guide all practitioners to pay attention to cultivation at the Purpose layer, avoiding the risks brought by "technology without ethics."
·Explainability and Trust: The process of DIKWP evaluation itself is transparent and explainable (similar to conducting a "white-box evaluation" on AI): every talent evaluation result can be traced back to the basis on each module. In contrast, traditional comprehensive scoring often makes it difficult to explain why someone is "excellent" or "insufficient." Explainable evaluation enhances talents' trust in the system, and also makes it convenient for decision-makers to explain the reasons for introducing and using talents to the public or superiors, improving decision-making transparency.
In summary, using the DIKWP model to improve high-level talent evaluation standards can achieve a leap from concept to semantics: allowing talent evaluation to change from simply listing conditions to a deep description of the full spectrum of talent capabilities; achieving a transformation from static to dynamic: allowing talent evaluation to be integrated into the entire cycle of talent growth, reflecting changes in a timely manner; achieving collaboration between individuals and organizations: through semantic matching, the talent's knowledge and strengths are precisely matched with the job's requirements, achieving the optimal state of "making the best use of talent, and placing talents in the right positions." For the burgeoning future industries of AI and artificial consciousness, such an evaluation and development mechanism will help build a high-level talent team that is both specialized in various fields and collaboratively evolving, providing a steady stream of power for industrial innovation. Human-machine co-evolution, intelligence and Purpose integration, we look forward to the DIKWP-perspective talent evaluation system moving from theory to practice in the near future, truly taking root, and serving our country's technological innovation and industrial upgrading strategy.
References:
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