Celebrating 25 years on the path to inclusive education
Bibliographic record
Abstract
This book celebrates 25 years of the European Agency for Special Needs and Inclusive Education. It begins with messages from the Agency’s Chair and the Director, followed by sections on: The organisation and its member countries: it looks at the Agency’s work for and with member countries. Looking back: the Agency’s history across a quarter of a century. Reflections on changes and developments in thinking around inclusive education. Looking forward: considering what the future may hold for the Agency. Throughout this book, there are statements from colleagues and member country representatives outlining the Agency’s influence on them and their work. The book serves as a snapshot of the Agency’s work. The information here should interest those who have followed the Agency’s progress throughout the years, as well as those new to its work.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.001 | 0.023 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".