The need for equality in education: An intersectionality examination of labeling and zero tolerance practices
Bibliographic record
Abstract
The authors observe that students in school who exhibit challenging behaviours are given labels such as “severe behaviour,᾿ “troubled,᾿ or “violent᾿ and that these negative labels have repercussions on students. School administrators also employ zero tolerance policies without addressing the root causes of negative behaviour. Using students’ self-reports the authors note the negative effects of labeling and zero tolerance practices on children and schools, and discuss the implications for society as a whole. They conclude with recommendations for changes in policies and practices that more carefully consider the systemic sources of the behaviour, and that align more closely with fundamental educational goals and the Canadian Charter of Rights and Freedoms. LE BESOIN D’ÉGALITÉ EN ÉDUCATION : UN EXAMEN INTERSECTIONEL DES PRATIQUES D’ÉTIQUETAGES ET DE ZÉRO TOLÉRANCE RÉSUMÉ. Pour contrôler les étudiants qui montrent des comportements violents à l’école, les éducateurs les catégorisent et les étiquettes par des termes de comportements sévères et utilisent des politiques de tolérance zéro. En utilisant des autoportraits d’étudiants, des jurisprudences, des résultats d’études à grandes échelles, et en prenant en considération une école exemplaire, nous notons les aspects négatifs de la politique de zéro tolérance, de l’étiquetage des élèves et nous discutons les implications de la société dans son ensemble. Nous concluons avec des recommandations, pour des changements dans les politiques et les pratiques, qui considèrent plus prudemment les sources systématiques du comportement, et qui se rapprochent plus étroitement des buts fondamentaux de l’éducation et la Charte canadienne des droits et libertés.
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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 | 0.020 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.055 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".