When RateMyProfessor Meets the #MeToo Movement: Bottom-up Bullying in Academia
Bibliographic record
Abstract
The #MeToo and Time's Up movements have created increased awareness around inappropriate work behaviour.According to these two partially overlapping movements, some organizations have been permissive in enabling employees to misuse their positions to assert undue power over others.Managers also recognize that complaint processes may be inadequate in resolving these issues.This paper uses a #MeToo lens to investigate student bullying, mobbing, and sexual harassment enabled through RateMyProfessor.com(RMP).For this research RMP is used as a proxy for social media sites that are visible and curtained to public viewing.It also considers how the need for the academy to operate as a hyper-commercialized business may be contributing to the silence of universities on the misuse of sites such as RMP.Main research questions include the following.Why is harassment of faculty through social media sites such as RMP permitted and even valued?How do social media sites' content and audience differ from university evaluations of faculty?
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".