Off the beats and track: Finding historical lesbian and queer women’s feminist spaces through musicians’ tour schedules, concert flyers, and correspondence
Bibliographic record
Abstract
This article explores historical research methods used to locate lesbians and queer women, especially within American and Canadian contexts from the 1960s onward. It begins by discussing methods such as analyzing women’s and lesbian travel guides, directories, maps, periodicals, newsletters, newspapers, websites, oral histories, social media, archival fonds and collections. In particular, this article explores how utilizing lesbian and queer women musicians’ tour schedules, calendars, correspondence, and contracts for shows and appearances can be a valuable historical research method, especially for locating impermanent historical lesbian and queer women’s spaces off the beaten track. The article focuses on the Alix Dobkin Papers as a case study to explore aspects of historical lesbian and queer women’s spaces and demonstrate the utility of this historical research method beyond Dobkin. The papers of Alix Dobkin include business correspondence, fan mail, fliers and programs from concerts, subject files, t-shirts, photographs, and memorabilia. As Dobkin played an important role in the women’s music movement and toured regularly, her papers provide useful insight into historical debates about lesbian anti-racist politics, ethical consumption, community organizing, and transgender inclusion and exclusion.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".