“All are welcome here?”: Navigating race, class, gender, sexual orientation, age, and disability in American feminist coffeehouses of the 1970s and 1980s
Bibliographic record
Abstract
Abstract In the 1970s and 1980s, feminists in the United States began businesses in order to financially support themselves while enacting women's movement politics. Owning and operating a feminist business in a permanent location required large capital investment in order to rent space, buy supplies, and pay workers. Women's, and especially lesbians', social positioning due to discriminatory gender, racial, and sexual orientation laws affected whether or not being able to own a restaurant or store was even possible. Coffeehouses provided an alternative means through which to build feminist communities, with lower initial capital required than restaurants. Feminist coffeehouses in this article will primarily refer to recurring temporary public spaces that served refreshments and provided entertainment. This model of feminist organization expanded participation in some ways; without high fixed costs, coffeehouses enabled women with less money, women from marginalized racial groups, and women with marginalized sexual orientations the ability to create spaces centered on food, drink, and socializing. However, these spaces were not utopic; racism, ageism, trans‐exclusionism, and classism created tension in their respective communities. As coffeehouse organizers tried to address various inequalities, their approach in treating each identity category as discrete often erased women who experienced multiple forms of oppressions simultaneously.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".