Preparing School Evaluators: Hiroshima Pilot Test of the Japan Evaluation Society’s Accreditation Project
Bibliographic record
Abstract
Abstract: This article reports on the efforts of the Japan Evaluation Society (JES), in collaboration with the Canadian Evaluation Society, to develop and pilot test an accreditation and certification scheme for school evaluators. The purpose of the JES accreditation model is to support evaluation capacity building and promote high quality evaluation by developing functional evaluation competencies. The article describes the theory and practice of the JES approach to evaluation training and accreditation, including its overall rationale, the influence of Japan’s socio-political context, the content of the school evaluator training program, and the findings of the initial “test of concept” pilot test in Hiroshima. Based on a six-month follow-up evaluation, the article also provides an assessment of the acceptance, early results, and potential sustainability of the evaluator training program. These findings have encouraged the JES to establish the accreditation scheme for school evaluation, followed by a similar system for the evaluation of international development assistance programs and government policy evaluation. The development of the JES accreditation scheme should be of interest to other evaluation societies and also to public/nonprofit organizations that must use brief training courses or evaluation “toolkits” for building evaluation competencies quickly among staff.
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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 | 0.056 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".