Strategies for instructor protection from false and frivolous human rights complaints
Bibliographic record
Abstract
False or frivolous human rights complaints against instructors, by students unsatisfied with their grades, have become a growing problem in some universities. These complaints and associated lengthy investigations are a form of mobbing that is harmful to instructors’ health and wellbeing. This in turn is harmful to instructors’ families, professional relationships, the pedagogical environment and the instructors’ careers. This paper reports on a brainstorming exercise used to identify possible ideas for preventing such false claims of human rights violations. This work operates under the assumption that the institution is unwilling or unable to improve their complaint management process. The authors identified 12 viable ideas that might help reduce the probability of a student making false accusations when unsatisfied with their grades. These ideas could be clustered as “Acquiesce”, “Shift Blame”, “Interaction Monitoring”, and “Separation”. All ideas had problem associated with them. Some ideas, like separation, were consistent with current pedagogical trends in distance and asynchronous education. In the face of a lack of institutional engagement in the current problem, the only idea identified that could secure instructor safety and wellbeing was to leave the profession. Further investigation is needed.
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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 | 0.033 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".