Taming the Raging Bully! A Case Study Critically Exploring Anti-bullying Measures to Support Neurodiverse Employees
Bibliographic record
Abstract
Disclosure of neurodiversity in the workplace can attract unfavourable attention. The aim of this case study is to critically investigate the collective potential of specialized and generic mental health promotion guides to help prevent or treat the bullying of neurodiverse employees. Applying qualitative thematic analysis to eight of these guides originating from Australia, Canada and England, this research offers three key messages that should be of interest to policymakers and practitioners working in the Asia-Pacific region and elsewhere. First, guides as reviewed by this study collectively support anti-bullying themes across dimensions of policy/procedures, education, legal, leadership and monitoring/support. Second, evidence sourced from scholarly and grey literature raise challenges that if overlooked might reduce the effectiveness of guide endorsed anti-bullying measures. Finally, this study raises the prospect that anti-bullying measures to assist mentally diverse staff might be more effective when potential synergies between these are recognized and encouraged.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".