Exceptionality Education International: Responding to Change and Promoting Dialogue on Inclusive Education for All
Bibliographic record
Abstract
Exceptionality Education International (EEI) provides a forum for research and dialogue on topics relevant to the education of people with exceptionalities and on how barriers to the full participation of all people in education can be reduced and removed. As current editors of this journal, we provide you with an overview of the editorship of the journal over the years, address changes to policies and practices in inclusive education, and explain how our journal has adapted to reflect these changes. We conclude with concerns currently being experienced by journals, and plans for moving forward with addressing these issues and maintaining a journal in the 21stcentury. Résumé Exceptionality Education International (EEI) fournit un espace pour la recherche et le dialogue sur des sujets relatifs à l’éducation de personnes comportant des « exceptionalités » et sur la réduction et l’élimination d’obstacles empêchant à quiconque de participer pleinement en éducation. À titre de directeurs, nous vous présentons un survol de la direction de cette revue au fil des années, traitons de changements aux politiques et pratiques en éducation inclusive, et expliquons comment notre revue s’est adaptée pour refléter ces changements. Pour conclure, nous décrivons les problèmes auxquels les revues font face aujourd’hui ainsi que nos projets pour résoudre ces problèmes et maintenir une revue au 21esiècle.
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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 | 0.061 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.035 | 0.039 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.013 | 0.029 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".