Inclusive education policies – objects of observance, omission, and obfuscation: ten years on …
Bibliographic record
Abstract
This article draws upon a critical policy analysis approach to examine the state of inclusive education policy in Global North settings over the past decade.Building on an earlier paper on this topic ten years ago, this updated article seeks to explore whether and how inclusion and inclusive education have been understood in varied international, national and sub-national policy settings over time.While it might be anticipated that schooling systems should be more aware and proactive in supporting inclusion within their policies, our findings reveal mixed results.At times, there appears to have been regression in some settings, stagnation in others, as well as moderate progress in other settings in relation to support and advocacy for inclusion in educational policies.In an era of increased attention to a range of issues of inclusion more broadly over the past decade (e.g.marriage equality, gender fluidity, Black Lives Matter, #StopAsianHate), our article cautions against assuming that such movements have somehow led to a more 'inclusive' conception of students' identities and wellbeing in schooling policy.Whether and how teachers can be expected to be more inclusive in their practices in such a variegated policy environment is an area for continued inquiry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.020 | 0.033 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".