Dwelling with feminist media archives in the age of big data
Bibliographic record
Abstract
Grounded in data feminism and critical data studies, this paper addresses the risk that uncritical uses of big data pose to the support and maintenance of feminist digital activism histories. We draw on recent findings from our work with the Archives Unleashed Cohort Program (2021-2022), comparing two #MeToo archives: the collection housed at Schlesinger Library’s digital holdings and an open access data visualization of #MeToo (https://ruebot.net/visualizations/metoo/). We highlight the overemphasis on #MeToo as solely a media event in the Schlesinger archive, producing a sanitized, white-centric, cis-heteronormative history that is far removed from questions of gendered and racialized sexual violence at the heart of the “me too” movement. We then discuss the value of social media data visualizations as an archive that is more capacious and accessible for various modes of scholarly analysis. Finally, we dwell with the data visualizations to demonstrate how this practice allows for greater understanding of the complex meaning contained within the data. In doing so, we reveal how embodied research methods help scholars name what is obscured within networked practices and discourses under the label of big data trends, generalizations, and patterns and, ultimately, propose alternative, more feminist, ways forward.
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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 | 0.041 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.016 | 0.058 |
| Scholarly communication | 0.030 | 0.045 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".