Inclusive Education in the Context of Ethnocultural Diversity: Understanding the Process of Exclusion to Act in the School — A Secondary Publication
Bibliographic record
Abstract
This paper reveals that the implementation of inclusive education is an unfinished challenge, both within the system and for individual self-improvement. This process of changing practices, by continually questioning the school’s responsibility for the (re)production of inequalities, exclusion, and unequal social relations, is riddled with obstacles, unpredictable situations, and strong emotions. In particular, the researchers point out that many systemic mechanisms of school culture contribute to replicating and reifying hierarchical school experiences and exacerbating processes of institutional discrimination against immigrant backgrounds and/or racialized students. The empirical research presented also highlights the school staff’s deficit thinking toward immigrant students and their parents. The results show that staff tend to use linguistic and cultural gaps between students and the school system to explain academic failure. Be that as it may, the researchers as well as the school actors and students interviewed in this paper suggest multiple ways to improve inclusion in the school context, stressing the importance of giving voice to the various actors in order to move toward institutional transformation.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".