A Historical Sociolinguistic Perspective on Bilingual LGBTQ+ Communities
Bibliographic record
Abstract
There has been extensive work on the role of bilingualism and multilingualism in speaker identity construction. There has been sociolinguistic ethnographic work that looks at the intersection of bilingual and queer identities (cf. most recently, Holly R. Cashman’s Queer, Latinx, and Bilingual, 2018). My research interests lie in the historical sociolinguistic setting of this interaction. Specifically, I am interested in the language practices of LGBTQ+ communities in metropolitan areas of historical linguistic conflict with the state. Presently, Montréal, Canada (French/English) and Barcelona, Spain (Catalan/Spanish) are my areas of focus. My intentions, broadly speaking, are to examine the impact of social and political implications of language choice on how LGBTQ+ individuals engage with their identities in the social narrative. By the time of the conference, I intend to be fleshing out my methodology after preliminary contact with the archives where I will be collecting data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".