(Re)framing Big Data: Activating Situated Knowledges and a Feminist Ethics of Care in Social Media Research
Bibliographic record
Abstract
In this article, we seek to problematize assumptions and trends in “big data” digital methods and research through an intersectional feminist lens. This is articulated through a commitment to understand how a feminist ethics of care and Donna Haraway’s ideas about “situated knowledge” could work methodologically for social media research. Taking up current debates within feminist materialism and digital data, including big, small, thick, and “lively” data, the argument addresses how a set of coherent feminist methods and a corollary epistemology is being rethought in the field today. We consider how the “queering” of Hannah Arendt’s concept of “action” could contribute to a critically optimistic and inclusive reflection on the role of ethical political commitments to the subjects/objects of study imbricated in big data. Finally, we use our recent research to pose a number of practical questions about practices of care in social media research, pointing toward future research directions.
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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 | 0.114 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.161 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".