MétaCan
Menu
Back to cohort
Record W4413252498 · doi:10.23977/aetp.2025.090412

Reform and Exploration of Talent Cultivation in Electrical Engineering: A Demand-driven, Competency-Based and University-Industry Collaborative Approach

2025· article· en· W4413252498 on OpenAlexvenueno aff

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
FundersDivision of Graduate EducationUniversity of Shanghai for Science and Technology
KeywordsEngineering managementEngineeringSupply and demandBusinessKnowledge managementEngineering ethicsComputer scienceEconomics

Abstract

fetched live from OpenAlex

To address the issues in the process of postgraduate talent training within higher education institutions, such as disconnection between disciplinary development and industry demands, insufficient innovation and engineering practice capabilities, and an underdeveloped university-industry collaboration education mechanism, this study explores an excellence-focused talent cultivation model characterized by being "demand-driven, competency-based and university-industry collaborative". Through reform pathways including utilizing demand-driven to clarify cultivation objectives, employing a competency-based approach to restructure the cultivation system, and innovating the educational model via university-industry collaboration, it aims to establish a "three-in-one" integrated talent cultivation reform model. This model seeks to foster an industry-education integration ecosystem featuring co-creation of talent, shared resource development and joint achievement sharing, alongside a dynamic deform mechanism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.363
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Educational Technology and PsychologySame topicHigher Education Learning PracticesFrench-language works237,207