Teachers, policymakers and project learning: The questionable use of ‘hard’ and ‘soft’ policy instruments to influence the implementation of curriculum reform in Hong Kong
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".