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 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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".