Bibliographic record
Abstract
Divided into two parts, this volume first discusses the concept of school-based evaluation, followed by a presentation of case studies of school evaluation from across the world. In part one, school-based evaluation is examined from three perspectives: school-based evaluation as a dialogue between internal and external evaluation; school evaluation from a perspective of institutional self-evaluation in a democracy and issues of definition, methods and implementation. The second part of the book presents case studies from Norway, England, The Netherlands, Austria, Spain, United States, Canada, Israel, Scotland and Germany. All of the case studies are based on actual experience with school-based evaluation in various educational and social contexts. Authors recognise the wide range of local constraints and reflect upon multiple evaluation perspectives, describing their educational context, evaluation perspective and specific school-based experience. They highlight difficulties encountered in their work, discuss the implications and make recommendations for further development of the concept of school-based evaluation and its practice. "School-Based Evaluation: An International Perspective" does not suggest internal/self school evaluation as an alternative to external "objective" evaluation. However, an attempt is made to advocate the combination of both for the benefit of school accountability and school improvement
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".