Bibliographic record
Abstract
With an aim to investigate inclusion across borders, quantitative and qualitative data were examined that came from 18 countries: Australia, Brazil, Canada, Cyprus, Finland, France, Iceland, India, Ireland, the Netherlands, Norway, Portugal, Slovenia, South Africa, Spain, Sweden, the UK, and the USA. Four themes emerged in this study: (1) concerns with student outcomes in inclusive settings; (2) teachers’ beliefs and the ways they relate to their practices; (3) inclusive schools philosophies and practices; and (4) the intersection of inclusion and exclusion. Although international policies, knowledge, and understanding about inclusion and inclusive practices continue to advance throughout the world, there are manifestations of provincialism and discrimination predominantly marked by ethnicity, socio‐economic status, and ability. While a reality for some students, inclusion remains an aspiration for too many.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".