Bibliographic record
Abstract
This study investigates how some U.S. women are engaging in projects of solidarity around war on the Internet, how their positioning is represented, and how the site authors reach toward what MarAÂa Lugones describes as A¢â¬AliminalA¢â¬Â spaces for coalition. While Lugones views the ambivalence in liminality as A¢â¬Aboth a communicative opening and a communicative impasseA¢â¬Â (76), she sees possibilities if we continually keep in mind that we do not know another person and that we resist fitting her into a prefabricated narrative (84). This paper is part of a roundtable discussion on feminist solidarity with Layne Craig and Erin Hurt (A¢â¬ATheory and PraxisA¢â¬Â), Morgan Gresham (A¢â¬ACreating Feminist SolidarityA¢â¬Â), and Jessica Restaino (A¢â¬AMother RhetoricsA¢â¬Â). The websites examined are Women Against War and American Women in Uniform, Veterans Too!; this project outlines the ways in which these websites accomplish countering ignorance, contributing to solidarity around women and war, and forwarding feminist projects.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".