Bibliographic record
Abstract
This special issue of American Speech offers a survey of the ways that place, region, and community intersect in contemporary dialectology and sociolinguistics.It contributes to the body of scholarship on regional linguistic variation by complicating the notion of region and what that means for different communities at the many and complex intersections of gender and sexuality, along with other categories of identity.These articles include analyses of intersectional regionality in the identity construction of Oklahoma City drag queens, transnational divergence of nonbinary neologisms in Quebec French, queer language differentiation in Jewish-English-speaking communities in Seattle, and disciplinary constructions of researcher identity in contemporary American dialectology and its antecedents.Taken together, these articles represent a reconsideration of the role of place, region, and community within the context of gender, sexuality, and language.This issue began in the height of the global pandemic, during which linguistics organizations like the Linguistic Society of America and the Associao Brasileira de Lingstica launched digital series covering various aspects of linguistic research.Both of these organizations featured talks on various research in LGBTQ+ linguistics, and both of us took part in these series.In them, two ideas emerged: that queer and trans people had not been included in much dialectological work focused on region and that noncisgender and nonheterosexual experience can complicate and make complex the notions we have about place and region in sociolinguistics.These observations led to a call for research on the complexity and intersectionality of place within (socio-)linguistics and what that meant for LGBTQ+ speakers, and we are proud to say that the articles featured here do so in novel and important ways.Bryce McCleary's article addresses a community of drag performers in Oklahoma City, in a historic site for LGBTQ+ communities in a city and state that are often hostile to such communities.Additionally, the participants describe this safe space and what it means for them as LGBTQ+ Oklahomans and as participants in drag culture in the city.The data are analyzed and found to highlight not only their awareness of language, place, and community ( la Preston 2010) but also their identities and experiences (e.g., Bucholtz and Hall 2008;Hall 2013) in the cultural landscape of the Oklahoma City drag scene.What emerges is a sense that diversity must be fought for even in such safe spaces, particularly along racial lines, and that competition for spotlight and success as a performer muddles the "safety"
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".