Dwelling as method: Lingering in/with feminist curated data sets on Instagram
Bibliographic record
Abstract
This article proposes and delineates “digital dwelling” as one method of grappling with a central methodological challenge that we, as feminist researchers, face of how researchers might account for the multiple entanglements of affect, history, culture, politics, and resistance within feminist digital media artifacts. Using our method of digital dwelling, we analyze three sets of carousel posts on Instagram from three different accounts: Intersectional Environmentalist Collective, For the Wild, and Richa Kaul Padte. We explore how the inter, para, and meta-textual arguments curated through these carousel posts change the ways audiences relate to one another and to the current political moment, and how audiences, including individual researchers, are situated in affective and embodied ways within the research scene. By demarcating small, embodied data curation as a key space of method and analysis, we suggest that the personal relationships we develop in community as researchers with located acts of transgression, like these posts, are significant to consider more fully through their emergent intertextualities, especially for those invested in contemporary social media, protest, and visual cultures.
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.
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 | 0.042 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".