Polarising Sexualities and Genders
Bibliographic record
Abstract
<JATS1:p>This ground-breaking open access collection tackles polarisations around sexualities and genders and in doing so, it opens out debates and discussions to reflect on how people live them, including and moving beyond social movements and political debates.</JATS1:p> <JATS1:p>Anti-gender and heteroactivist movements have become increasingly recognized as key political groups in the last decade, driving resistances that often respond to LGBTQI+ inclusion. How these divisions are felt and experienced is under-explored.</JATS1:p> <JATS1:p>Crossing a variety of geographical contexts, including India, USA, Canada, Ireland, and Great Britain, Polarising Sexualities and Genders brings together leading and emerging scholars in multiple disciplines to explore, theorise and hope for change in social, legal, and political sexual/gendered/LGBTQIA+ landscapes. Examples include polarisation in media coverage and legislative debates about gender and transgender rights in the US and UK, to the possibility of occupying a sexual borderlands, the changing language for LGBTQIA+identities within the US, and the ways in which new discourses emerge to capture sexual lives. Through these case studies, the book explores how polarizing discourses and sociopolitical landscape shape the lived experiences, divisions, and marginalizations among LGBTQIA+ people in places including Lithuania, South India and the southern United States.</JATS1:p> <JATS1:p>The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by the European Research Council.</JATS1:p>
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".