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Record W2056289680 · doi:10.1080/02255189.2003.9668896

Working and Worrying Partnership: A Case Study of a Long-Term Academic Development Project in China

2003· article· en· W2056289680 on OpenAlexvenueno aff
Dwight Boyd, Julia Pan

Bibliographic record

VenueCanadian Journal of Development Studies/Revue canadienne d études du développement · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipChinaTerm (time)Context (archaeology)Focus (optics)Work (physics)Political scienceSociologyPublic relationsPedagogyEngineering ethicsEngineeringGeography

Abstract

fetched live from OpenAlex

This paper consists of a case study of a long-term academic development project in China. The background and framework of the project as a whole are first described, and then the particular context and goals of work in three focus area.—gender studies, moral education, and minority and bilingual education—are elaborated in more detail. Some of the more notable results and their impact are identified for each focus. Attention is then given to an evaluative account of special challenges faced, how they were approached, and worries about structural obstacles to truly collaborative parmering.

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)0.010
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0300.009
Scholarly communication0.0040.003
Open science0.0040.009
Research integrity0.0040.004
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 head0.224
GPT teacher head0.391
Teacher spread0.166 · 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 designQualitative
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
Published2003
Admission routes1
Has abstractyes

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