Putting People First: Unpacking the Relationship Between Social Media Influencers and Feminism
Bibliographic record
Abstract
ABSTRACT This narrative review synthesizes knowledge at the intersection of social media scholarship, the role of influencers in disseminating information about feminist causes. Feminist activism on social media, such as hashtag activism like #MeToo, has received much attention from scholars, but the role of influencers in disseminating information remains understudied. Much of the past research has examined the commercial nature of influencers. We conducted a narrative review using the search results from eight academic databases. We examined three research questions: (1) what types of social media influencers have been studied, (2) what feminist approaches were drawn on, (3) and what are influencers' functions in the dissemination of information on feminist causes. We found that the literature had covered influencers from many parts of the world whose content focuses on various areas of life, with some specifically advocating antifeminism. We also found that feminist theoretical approaches, mainly surrounding neoliberalism and post‐feminism, have informed much of this research. Finally, we found that the studies within feminism as challenging norms and expectations, calling out social issues, and building community. From these findings, we derive directions for future studies and the continuation of our project.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".