S’approprier la psychologie queer: la valeur de l’intégration de la théorie queer et de la psychologie dans l’autoethnographie critique
Bibliographic record
Abstract
This commentary reflects upon an Honours thesis undertaken in 2024-2025 to analyze the subjective experience of coming out in adulthood through critical autoethnography. The method used, which blends autobiography with ethnographic observation, was rooted in psychological frameworks and drew upon queer theory—a scholarly perspective that challenges and attempts to disrupt heteronormative assumptions of gender and sexuality—to analyze the first author’s experience of changes in sexual orientation in her thirties. The authors discuss the challenges, and ultimate benefit, of interweaving psychology, which tends to be grounded in positivist and structured views, with queer theory, which promotes fluidity and resists established norms. Sharing their unique perspectives, each author contributed to this essay from their respective discipline, highlighting the possibilities that appear when holding two seemingly opposing theoretical tensions; not just in understanding experiences of diversity among sexual orientation identities, but also in questioning the traditional boundaries of research and the complexity of human experience. As researchers who are also part of the queer community, the authors found great value in queering psychology scholarship, supporting a need for diverse representation within academia.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.067 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".