#MeToo and Canadian University Students
Bibliographic record
Abstract
•#MeToo can be described succinctly as an online social movement against sexual harassment and sexual violence. •#MeToo emerged with significant “viral force” in mid-October of 2017 (Tambe, 2018, p. 197). Ostensibly, #MeToo arose in response to public revelations regarding persistent patterns of sexual harassment and sexual assault against female workers within the U. S. media and entertainment industries. Over the days, weeks, and months that followed, the apparent reach, scope, meaning, and impact of #MeToo developed rapidly; #MeToo, as a social movement, continues to evolve. •The virality of #MeToo, at least in part, likely reflected its close connection to celebrity culture (Tambe, 2018). •Whilst #MeToo arose within a particular social, historical, and political context, it reflects a well-established tradition of online sexual activism (e.g., Keller, Mendes, & Ringrose, 2018; Martsenyuk & Phillips, 2020; PettyJohn et al., 2019). •#MeToo has been the focus of both high praise and sharp “backlash.” Proponents of #MeToo suggest that the movement has cast a powerful spotlight on sexual violence – in particular, the breadth, complexity, and ubiquity thereof. Some have suggested further that #MeToo has provided a transformative online “public speak-out” for survivors of sexual violence (i.e., a virtual space for disclosure, support, solidarity, empathy, and empowerment among survivors). Alternatively, criticism of #MeToo has included the following: that the movement likely promotes “false allegations” of sexual harassment and sexual assault, that it undermines fairness and due process for those persons accused of sexual misconduct, that it has a negative impact on contemporary courtship behaviours, that it adversely impacts sex/gender relations (both in the workplace and more broadly), and that it variously undermines the status of women and girls. •Some have suggested that #MeToo is far more inclusive of certain groups than of others. Specifically, it has been argued that #MeToo disproportionately emphasizes the experiences of privileged, relatively affluent, white, cis-gendered women (e.g., Evans, 2018; Leung & William, 2019; Tambe, 2018; Zarkov & David, 2018). Some have suggested that Persons of Colour, transgendered individuals, and other diverse persons are underrepresented (if not excluded entirely) by the #MeToo movement (e.g., Hemmings, 2018; Johnson III & Renderos, 2020; Lee, 2018). Of those groups said to be underrepresented or excluded by the #MeToo movement, many are known to be impacted disproportionately by sexual violence (e.g., Burcycka, 2019; Cantor, 2015; Rotenberg, 2017; Wincentak et al., 2017). •The present study seeks to examine if, in what form, and to what impact #MeToo has been taken up by Canadian undergraduate university students. Previous research suggests that university students are at elevated risk to experience sexual violence (e.g., Muehlenhard et al., 2015; Bergeron, Goyer, Hébert, &Ricci, 2019). Study data are currently being collecting through the administration of an anonymous online survey instrument; the survey instrument is being administered to university undergraduate students at both campuses of a large Canadian university (Campus 1 and Campus 2). These data are being collected within the context of a large-scale study examining the prevalence of unwelcome/unwanted sexual experiences among Canadian undergraduate university students. •Two previous published studies have attempted to examine how Canadian undergraduate university students construct/conceptualize #MeToo (Carey et al., 2022; Williamson et al., 2020). Both of these studies relied on a face-to-face focus groups design. These previous studies were marked by a number of strengths and weaknesses. The present study will be the first to examine how Canadian undergraduate university students construct/conceptualize #MeToo using an anonymous online survey – this design offers a number of distinct advantages over the previous studies. This study will be the first to explore directly the degree to which (if at all) Canadian undergraduate university students consider themselves to be familiar with #MeToo. This study will be the first to explore directly if and how Canadian undergraduate university students perceive that #MeToo has impacted their personal dating, romantic, and sexual behaviour. This study’s analytic strategy will rely on a combination of quantitative content analysis (QCA), parametric statistical analyses, and non-parametric statistical analyses.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.042 | 0.007 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.089 | 0.008 |
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".