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Record W2804902185 · doi:10.5539/jel.v7n4p66

Influence of the Rewards and Recognition Scheme on Higher Vocational Education Curriculum Reform in China

2018· article· en· W2804902185 on OpenAlexvenueno aff
Hao Chen, Mark A. Tyler, Richard G. Bagnall

Bibliographic record

VenueJournal of Education and Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumVocational educationChinaGovernment (linguistics)Scheme (mathematics)Mathematics educationPedagogyCurriculum mappingQualitative researchCurriculum theoryCurriculum developmentPsychologyPolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

This paper reviews the impact of the rewards and recognition scheme on curriculum reform in higher vocational education (HVE) in China. In this scheme, teachers and students can win rewards and obtain recognition through curriculum competitions and student skill competitions conducted by the government. It has been used to encourage and to support colleges and teachers to implement HVE curriculum reform. The qualitative research project into HVE curriculum reform reported here identified both the facilitative and inhibitory effects of the scheme on curriculum reform. Discipline heads’ perspectives of these influences were investigated through in-depth interviews. The scheme was seen as motivating just a slight majority of discipline heads to implement curriculum reform and as providing financial support in doing so. The discipline heads, though, also indicated that they were discouraged by the scheme’s misleading guidelines, its inappropriate evaluations of their efforts and its contribution to funding inequalities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.363
Teacher spread0.346 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2018
Admission routes1
Has abstractyes

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