Bibliographic record
Abstract
Behaviour management is an influential educational cliché in Australia, Canada, England, New Zealand and US. In practice efforts to control student conduct in schools frequently utilise a manage and discipline model: a misinformed but deeply-rooted set of interconnected notions about how to ensure an orderly and productive classroom. Students with disabilities affecting their behavioural development or who have mental health difficulties (MH) frequently face disadvantage, suspension or exclusion as a result of the application of this model in practice. Accommodating the behavioural needs of this population and at the same time, enabling their inclusion therefore represents a significant wicked problem for education in Australia, Canada, England, New Zealand and US. Evidence-based, initiatives designed to address this dilemma in the US since the late 1990s, using PBS (Positive Behaviour Support) and also SWPBS (School-Wide Positive Behaviour Support) are outlined but the conclusion is reached that these efforts do not appear to have been successful. Recommendations are made for progress in tackling this wicked problem and include: wholehearted rejection of the manage and discipline model by practitioners; targeted support for teachers experiencing (or at risk of experiencing) occupational burnout; and the introduction of tangible educational policy incentives intended to encourage schools to include students who might otherwise face suspension or exclusion on behavioural grounds. Finally, this article advocates radical change in attitudes by teachers toward student conduct in schools and argues that educational practice should align with insights about human behaviour arising from research in developmental psychology.
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 | 0.039 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.014 | 0.038 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.010 |
| 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".