Research Ethics with Gender and Sexually Diverse Persons
Bibliographic record
Abstract
Identifying and developing inclusive policy and practice responses to health and social inequities in gender and sexually diverse persons require inclusive research ethics and methods in order to develop sound data. This article articulates 12 ethical principles for researchers undertaking gender and sexually diverse social, health, and related research. We have called these the 'Montréal Ethical Principles for Inclusive Research.' While writing from an international social work perspective, our aim is to promote ethical research that benefits people being researched by all disciplines. This paper targets four groups of interest: 1. Cisgender and heterosexual researchers; 2. Researchers who research 'general' populations; 3. and sexually diverse researchers; 4. Human ethics committees. This article was stimulated by the 2018 Global Social Work Statement of Ethical Principles, which positions human dignity at its core. It is critically important to understand and account for the intersectionality of gender and sexuality with discourses of race, ethnicity, colonialism, dis/ability, age, etc. Taking this intersectionality into consideration, this article draws on scholarship that underpins ethical principles developed for other minoritized communities, to ensure that research addresses the autonomy of these participants at every stage. Research that positions inclusive research ethics at its foundation can provide a solid basis for policy and practice responses to health and social inequities in gender and sexually diverse persons.
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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 | 0.038 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".