Coalition and Multi-Positionality Research Teams: A Nuanced Approach for Anti-Oppressive Research
Bibliographic record
Abstract
Abstract This article delves into the typically unexamined complexities of conducting research with vulnerable populations, specifically focusing on trans and gender-diverse children and youth. While ethical guidelines exist, there are persistent knowledge gaps in genuinely collaborative research practices with vulnerable populations. Using collaborative autoethnographic methodology, the study draws on the narratives and reflexive accounts of four researchers to explore the complexities of oppression. The article argues for the necessity of a coalition of knowledge and multi-positional perspectives to develop sensitive and rigorous intervention strategies and policymaking. We propose the adoption of two theoretical frameworks: intersectionality and queer reflexivity, to guide our reflections and enhance research outcomes. By acknowledging and integrating diverse positionalities, collaborative approaches can increase the sensitivity, relevance and impact of research. The article proposes that recognising the intersectionality and temporality of researchers’ and participants’ identities can help to effectively navigate complex ethical, methodological and empirical research terrain. This we argue, ultimately contributes to more robust knowledge production, inclusive and impactful research outcomes. In conclusion, the study highlights the significance of embracing coalition and multi-positionality in anti-oppressive research endeavours that can provide diversified perspectives and interventions to better address the complex and multifaceted nature of oppression.
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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 | 0.177 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.023 | 0.084 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.005 | 0.040 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".