Selective Incivility, Harassment, and Discrimination in Canadian Sciences & Engineering: A Sociological Approach
Bibliographic record
Abstract
There is little scholarly evidence describing the gendered and racialized climate faced by women in Canadian academic sciences and engineering (NSE). We address this gap with a sociological examination of selective incivility, harassment, and discrimination amongst NSE faculty from 12 Canadian universities; asking if female and racialized female faculty, in particular, are more likely to experience mistreatment at work than their white, male colleagues. Analyses of survey data indicated that women were significantly more likely to be mistreated by their co-workers and students than male faculty. Moreover, harassment and discrimination were associated with greater professional marginalization for women, including delayed advancement. Thus, taking a sociological approach to interpersonal mistreatment emphasizes the connection between employee interactions and structural gender inequality in male-dominated NSE. We found mixed evidence with respect to race: racialized women reported less co-worker and student mistreatment than their white female counterparts, but these results were only marginally significant; and racialized men reported significantly more harassment and discrimination than white men. As such, our findings suggest the importance of investigating the organizational employment setting to better understand which workers are at greater risk for mistreatment in different job contexts.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".