A McMaster Undergraduate Study of the Social Origins and Implications of Slang and Gendered Language
Bibliographic record
Abstract
Previous American studies on university slang such as Pamela Munro’s UCLA Project have stated that there are more derogatory terms to describe women than men, and that there are more complimentary and varied terms for men. In “The Semantic Derogation of Women” and “Gender Marking in American English,” Muriel Schulz and Julia Penelope remark that women are mainly described in relation to men. Florie Aranovitch observes that women are labeled in positive and negative extremes in Munro’s Slang U. Finally, Schulz and Penelope claim that men are mainly using the derogatory terms for women. The purpose of this original study is to investigate how gendered words are used in a Canadian university context, particularly in McMaster University of 2005. The McMaster survey reveals that the number of complimentary and derogatory terms used to describe men and women are almost equal, and that traditional stereotypes of men and women still exist but are more relaxed. Another interesting result is that women appear to be using derogatory terms as often, if not more than men. According to this study, the usage of gendered language in McMaster University does not privilege men and degrade women to the extent that was maintained in previous American slang studies.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".