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Record W1492112064 · doi:10.22230/ijepl.2010v5n6a198

Teachers, policymakers and project learning: The questionable use of ‘hard’ and ‘soft’ policy instruments to influence the implementation of curriculum reform in Hong Kong

2010· article· en· W1492112064 on OpenAlexvenueno aff
Ping Kwan Fok, Kerry J. Kennedy, Jacqueline Kin Sang Chan

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumGovernment (linguistics)Political scienceSovereigntyPublic administrationPedagogyPublic relationsSociologyPolitics

Abstract

fetched live from OpenAlex

Following the return of Hong Kong to Chinese sovereignty in 1997, the government of the Hong Kong Special Administrative Region developed wide-ranging curriculum reforms, including project learning. A recent survey has indicated that over 80% of Hong Kong primary and secondary schools have adopted project learning as a curriculum task. Such an outcome is hard to reconcile both with the culture of Hong Kong schools and the generally bleak picture that pervades the literature on educational change. In seeking an explanation for this apparent success we focus attention on the policy instruments that were used by government agencies to facilitate the process of implementation. Our analysis revealed that teachers were caught in a pincer movement that involved voluntary activities promoting project learning and coercive measures that monitored and evaluated successful implementation. Teachers’ views of these policy instruments differed markedly from those of policymakers. This confluence of mixed approaches, while apparently successful, is also shown to be problematic. Finally, the paper is located in a theoretical framework with its origins in recent policy theory that to date has not been applied to educational contexts.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.087
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.140
GPT teacher head0.469
Teacher spread0.329 · 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

Citations19
Published2010
Admission routes1
Has abstractyes

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