Bibliographic record
Abstract
On January 21, 2017, the Women’s March in Washington, D.C., produced a striking protest image: two women standing shoulder to shoulder, fists raised, holding signs that called for solidarity across race, ability, gender identity, and class. Echoing a 1971 portrait of Dorothy Pitman Hughes and Gloria Steinem, the image gained significant traction online—celebrated, critiqued, and debated across social media platforms. This paper introduces the concept of a “collective space” to describe the emotionally charged digital arenas, such as comment sections, where feminist discourse unfolds in real time. Drawing on Sara Ahmed’s theory of emotional stickiness and the frameworks of transnational feminism, I analyze 168 social media comments responding to this image, coding them for emotional tone and thematic patterns. The analysis reveals solidarity, critique, and identity negotiation occurring simultaneously, as users wrestle with feminism’s historical exclusions and its evolving intersectional commitments. By tracing these interactions, I show how collective spaces both foster belonging and reproduce exclusion, mirroring the tensions embedded in the broader feminist movement. These digital arenas are not incidental noise but vital sites of feminist praxis—spaces where emotions, histories, and politics collide, shaping the possibilities and limits of solidarity in the twenty-first century.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".