Does it really take a village to raise a child? Reflections on the need for collective responsibility in inclusive education
Bibliographic record
Abstract
Research in inclusive education reveals multiple studies that explore the efforts of individual stakeholders to create an equitable educational experience for students with disabilities. However, these individual efforts are often examined discretely, compartmentalising the contributions of various stakeholders. As a consequence, the complex interplay between these contributions has not been fully explored, with the capacity for a rich network of support being assumed rather than explicitly constructed. This report draws on the personal reflections of nine academics in the field of inclusive education from Australia, Canada, Germany, Greece, Italy, and Switzerland. Serving as both contributors and participants, this study draws together their personal interpretations and their expertise regarding the value of collective and collaborative inclusive education. Inductive thematic analysis of participant reflections yielded the view that stakeholders working together within an educational setting, offers more effective and appropriate opportunities to support learners with additional needs.
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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.045 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".