School Discipline and Administrator Decision-making
Bibliographic record
Abstract
School Discipline and Administrator Decision-Making has been developed for leaders, practitioners, and researchers to explore the theory and practice of decision-making, with specific reference to school administrators and their response to school discipline. This work delves into the experiences of Canadian and Mauritian principals faced with tensions―sometimes racial―regarding discipline. School leaders have the potential to make valuable changes within their schools as their influence touches on all aspects of the school building and culture. Yet, often discipline presents a challenge for school leaders as it requires a certain degree of discretion, especially when examining the issue of racial discipline disparity gaps in schools. Exploring both a Western and non-Western context, the opening chapters highlight the rigidity of behavioural policies and the desire to break from such neoliberal practices despite fears of repercussion in Mauritius. This is followed by an exploration of the decision-making of principals in Ontario and Nova Scotia in a Canadian context. The chapters challenge neoliberal policies and guidelines by questioning current practices and proposing various leadership frameworks. By incorporating both a Canadian and Mauritian context, the authors highlight various strategies which can be used to rethink our current approaches to decision-making.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".