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Record W2884838775 · doi:10.15273/ijge.2018.03.011

Zijin Mode: Industry and Education Deep Integration in Mining Engineering

2018· article· en· W2884838775 on OpenAlexvenueno aff
Wanzhong Yin, Weiran Zuo, Lixian Zhang, Yan Ping Fan

Bibliographic record

VenueInternational Journal of Georesources and Environment · 2018
Typearticle
Languageen
FieldComputer Science
TopicHigher Education and Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsDeep integrationProcess (computing)Mode (computer interface)Engineering managementSystem integrationComputer scienceKnowledge managementBusinessEngineeringDatabase

Abstract

fetched live from OpenAlex

This paper introduced the Industry and Education Deep Integration mode conducted by Fuzhou University in the process of Mining Engineering Education. The specific mode in Fuzhou University is named Zijin Mode, which can be briefed as “the enterprises support the construction of education, the enterprises participate in the education process, and the enterprises test the effectiveness of the education”. There are six integrations in the Zijin Mode, i.e. the integration of organization, the integration of experimental platform and teaching resources, the integration of teachers’ team, the integration of talent cultivation and use, the integration of teaching and the integration of science and technology innovation and transformation. The deep integration of industry and education in Zijin Mode provided guidance to Mining Engineering Education and worth popularizing.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.276
Teacher spread0.267 · 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 designOther design
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
Published2018
Admission routes1
Has abstractyes

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