“You don’t talk like a woman”: the influence of gender identity in the constructions of online misogyny
Bibliographic record
Abstract
The expansion of the Internet and social media has led to growing global interests in enhancing online safety of all categories of users. Nonetheless, the prevalence of online misogyny is worsening across virtual contexts. This study employs online interviews with Nigerian women on Facebook to examine the manifestations, effects, and strategies for navigating online misogyny. Findings reveal that feminism, a budding feature of the Nigerian social media is fast becoming a central motivating factor for online misogyny. Consequently, women’s increasing online engagements are sparking incidents of misogyny that consciously serve to limit their online voices and visibility. Women’s experiences of online misogyny are interrogated as iterative of mainstream patriarchal ideology while muted group theory portraying women’s positioning as a traditionally muted group in mainstream society is deployed to unpack the constructions of misogyny and silencing. Findings challenge common portrayals of social media as gender-neutral environments. Misogynists attack women who adopt feminist tags and those considered pushovers. In response, women adopt a two-tier strategy: “moral persuasion” or “going hard” to deal with online abuse. This approach constitutes serious emotional labour on the part of women and despite its utility, remains unsustainable in fighting online misogyny.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".