Out in Psychology: Lesbian, gay, bisexual, trans and queer perspectives
Bibliographic record
Abstract
If we are liberated we are open with our sexuality.Closet queenery must end.Come out.' 1 'We're out.Where the fuck are you?' 'Nobody knows I'm a lesbian.' 'We're here.We're queer.Get used to it'.'Trans, out and proud.' 'Blatantly bisexual.'These pride slogans and rallying cries for lesbian, gay, bisexual, trans 2 and queer (LGBTQ) movements -at once celebratory and confrontational -highlight the importance of 'outness' and visibility across a range of political eras and agendas.The title of this volume Out in Psychology: Lesbian, gay, bisexual, trans and queer perspectives, draws attention to the centrality of visibility for LGBTQ psychologies 3 , movements and politics.We chose this title to signal the presence, and increasing validation and acknowledgement, of research, theory and practice on LGBTQ concerns across the discipline of psychology.We are 'outing' psychology as a discipline that already, if sometimes ambivalently or unwillingly, incorporates LGBTQ perspectives.Although it is important to have a separate space to pursue research and practice, it is vital that we engage with, and contribute to, the broader discipline (Dworkin, 2002).LGBTQ psychologies of all varieties aim to support social change.This goal is realised both through making or assisting interventionsOut in Psychology: Lesbian, gay, bisexual, trans and queer perspectives.Edited by Victoria Clarke and Elizabeth Peel.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".