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Record W4415554698 · doi:10.1080/26895269.2025.2575868

“I was just some bimbo behind a desk”: trans and non-binary people’s experiences of harms as peer researchers

2025· article· en· W4415554698 on OpenAlexafffund
Merrick Pilling, Lori E. Ross

Bibliographic record

VenueInternational Journal of Transgender Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of TorontoCentre for Disability Prevention and Rehabilitation
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsQualitative researchAction (physics)Perspective (graphical)Quality (philosophy)Perception

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine the experiences of transgender and non-binary (TNB) people in peer researcher roles in order to contribute to an understanding of the extent to which peer research is effective in mitigating or reducing the potential harms of research for TNB people.Methods Semi-structured qualitative interviews were conducted with 13 TNB peer researchers who had been employed in the 10 years prior in a paid research position that required TNB lived experience. Data were analyzed using approaches drawn from thematic analysis.Results Four major themes were generated that captured the experiences of TNB people in their work as peer researchers: (a) imposition of cis-centric language and frameworks, (b) devaluing of community-based TNB knowledge, (c) experiencing discrimination and oppression on the job, and (d) the importance of anti-oppression and conflict resolution.Conclusion Careful interrogation of peer research practices is needed to mitigate potential harms and create meaningful engagement of TNB people. TNB people should be in positions of leadership from inception to completion of the research to ensure that research projects and teams are not built upon ciscentric and cisnormative frameworks that by definition devalue TNB lives and experiences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.311
GPT teacher head0.601
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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