(Re)framing Big Data: Activating Situated Knowledges and a Feminist Ethics of Care in Social Media Research
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
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.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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 it