Mapping #MeToo: A synthesis review of digital feminist research across social media platforms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A tweet by Hollywood actress Alyssa Milano using Tarana Burke’s phrase “me too” sparked a global movement. Despite the media attention #MeToo has garnered, little is known about how scholars have studied the movement. Through a synthesis review covering sources from 2006 to 2019, we learned that in this time period only 22 studies examined participation on social media such as Twitter and Facebook. We conclude that more research needs to be conducted, particularly to fill a gap in qualitative studies that directly engage individuals, to learn about their experiences with the movement. While #MeToo is a global movement, the omission of any reference to geography or a lack of geographic diversity suggests a narrow focus on scholarship based in the Global North. There is a need for more cross-cultural analysis to gain a better understanding of the movement as it evolves over time and moves into different spaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it