What does the village need to raise a child with additional needs? Thoughts on creating a framework to support collective inclusion
Bibliographic record
Abstract
In this paper, a group of nine international scholars reflect on the collective responsibilities of stakeholders within inclusive educational settings. This reflection was prompted by the need to identify specific elements which would support intentional, collective responsibility to support authentic inclusion for all students. In order to engender this collectivist mindset, mirroring the metaphor of the nurturing village, the group conducted a qualitative study based on structured and semi-structured dialogue, written reflections and previously constructed research to inform a framework to support inclusivity more collectively. Results suggest that nurturing spaces, empathetic relationships, supportive networks and targeted teaching, all contribute to bona fide inclusion, especially if this responsibility is shared and cohesive. Data further revealed that inclusivity is a values-driven process which flourishes when all stakeholders subscribe to common values and tenets regarding socially just educational provision. The authors inculcate the village-mindset, a now popularly received notion, reinforcing the need for active and deliberate dialogue focusing on shared responsibilities and vision. In this paper, we intend to reiterate the need for educational systems which foster more collective, compassionate and nurturing inclusive practice in educational settings.
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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| 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".