“It’s just one step in the right direction”: A qualitative exploration of undergraduate student perceptions of #MeToo
Bibliographic record
Abstract
As a social movement, #MeToo offers a chance for individuals to share their stories and connect with others who have been sexually assaulted or harassed. The movement may also facilitate understanding of the scope of sexual assault and harassment worldwide. Preliminary research on #MeToo has provided some insight on potential societal effects of the movement, but many research questions remain unanswered. The current study aims to contribute to the scarcity of research on the #MeToo movement. Through a series of focus groups, a sample of Canadian undergraduates (N = 56) were given the opportunity to discuss their views of why #MeToo is important, the role they think it plays, and their concerns. Students also explored both perceived positive and negative effects of #MeToo, as well as its potential sustainability. The social, structural, and gendered complexities involved in the emergence of the #MeToo movement were highlighted. Positive aspects of the movement that were emphasized included awareness raising, support for assault disclosure, and use of the media as an important tool. However, some individuals were concerned with media being used as a dangerous tool and that some groups have been harmed or excluded from #MeToo. While many participants felt that there is some evidence of #MeToo’s “success,” they believed that with respect to sexual assault and harassment more time may be required before sustained social and structural changes emerge.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".