Bibliographic record
Abstract
Educational administration is a rich domain of scholarship and practice, but one subject rarely discussed is its dark side. This study explored the question: What types of maladministration occur in schooling systems? The goal was to develop findings to inform existing prevention strategies. Focused on the Canadian context, data sources included 64 reports from disciplinary hearings of administrators in the Provinces of Ontario and British Columbia, complemented with other publicly available sources such as news stories. Findings indicated only a small minority of populations of administrators were subjected to disciplinary investigations and sanctions, but the targeted misconduct was often severe. Analysis revealed eight dimensions of maladministration, with sexual misconduct against students and financial transgressions being the most frequent. Academic dishonesty in the context of standardized testing and gendered patterns of maladministration also stood out. A typology emerged that highlighted the main forms of misconduct and negative leader behaviours against which schooling communities should bolster defences. When populated with data on the frequencies of acts of maladministration, the typology can help schooling communities to establish prevention priorities. The data in this study supports making the issues of sexual misconduct and the duty to report sexual abuse central to any planned interventions in the leadership system.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".