Who We Are, Where We Come From, and Where We Hope to Go in Educational Inclusivity
Bibliographic record
Abstract
This is a duoethnography of two teacher-researchers who have spent their lives immersed in architecture for education that is equitably inclusive. Ongoing engagement with people who have experience(d) educational exclusions, and consistent evidence that educational inclusivity benefits everyone, ensure that we remain committed to education being/becoming inclusive of all. Everyone has the right to educational inclusivity; we have found it both possible and effective for all concerned wherever/whenever fully attempted, when/where the hegemonic hold of special-education[1] is dismantled. It is time to focus on inclusivity rights of current and future generations of students, to stop debating whether inclusive rights are right for everyone. We propose that inclusive education can be realized by addressing five basic constructs rather than staying engrossed/mired in complexifying obstructions and costly defence thereof. After 35 years withstanding equivocation, we hope for widespread building of educational inclusivity; moving away from perennial advice/permission seeking from those determined to maintain status quo. As a start, we invite you into the conversation with us from connections with Canada, Ireland, and elsewhere.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.061 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".