The Research on the Pursuance of Hong Kong "Performance Indicators" and Its Inspiration for Us
Bibliographic record
Abstract
This thesis briefly reports the pursuance of the Performance and its effects. The research lasted three and a half years, including two periods, which evolved 15 experts, 1200 teachers and thousands of children. The first period was to explore and research into all kinds of practicing experiences and models with Performance Indicators, and during this period, Guide to using the pre-primary Indicators in Learning and Teaching was published, and it is for teachers' reference. The second period was to have a trial application of the Guide to using the pre-primary Indicators in Learning and Teaching to 64 pre-primary schools and to have a self-evaluation and perfection, which could test and verify the related experiences and models deeply. This research has cleaned the main obstacles for the final popularization of performance by Hong Kong Government and it is also the theory preparation. The thesis analyzes the experience and inspires the pursuance of the new Syllabus in the inland.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".