Globalised discourses of ‘challenging behaviours’ and implications for their construction and management in education
Bibliographic record
Abstract
The article adopts an exploratory lens to unravel the nature and construction of diagnostic labels associated with challenging behaviours while discussing their ideological underpinnings, politicized character, effects and policy implications. The non-normative nature of categories related to challenging behaviours calls for adopting a critical and cross-cultural perspective in exposing the intricately complex web of context-specific ideological and institutional dynamics that underpin the genesis, legitimation and subsequent management of these behaviours. The analytical edge also involves unravelling the political and socio-culturally mediated processes of constructing categories of ‘need’ based on deviant and disruptive behaviours while exploring how these normative discourses ‘travel’ globally are indigenised and pathologize human behaviours by, inter alia, prescribing clinical-orientated identification and behaviour management strategies. A cross-cultural lens is not only instrumental in bringing to the fore points of cross-national convergence/divergence and identifying examples of good practice but also in revealing the politically driven nature of categorical ascriptions and associated nomenclature of challenging behaviours, and understanding how they are constructed, disseminated and managed in contemporary schooling. As an action-oriented response to these critical considerations, the article makes a case for the imperative of adopting an interdisciplinary and intersectional approach to preventing and managing students’ behaviours in inclusive education.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.139 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.008 |
| 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".