Using positionality and reflexivity to support equity in partnership‐driven research
Bibliographic record
Abstract
Social and economic position and power shape everyone, including scientists and researchers. The way researchers do conservation science and the voices centered in the process are a result of researcher upbringing, experiences, access to resources, and values and are a manifestation of positionality. Positionality is a concept that can help one think about one's position and power in one's work. Creating a successful research partnership requires careful thinking about how equity, diversity, inclusivity, and accessibility are accounted for in the research environment. We drew on our own experiences as early career, mid-career, and Indigenous researchers to explore researcher positionality and how understanding one's positionality can bring to the fore power dynamics in conservation science and research. We focused on the use of reflexive practice to recognize diverse roles and responsibilities, build strong project governance, and enrich relationships. We considered 2 large research partnerships, Apoqnmatulti'k (Mi'kmaw for we help each other) and the SakKijânginnaniattut Nunatsiavut Sivunitsangit (Inuttitut for Sustainable Nunatsiavut Futures) project, to examine moments of tension and interrogation of power and the ways in which this interrogation led to stronger relationships and better research. We advise that large transdisciplinary and cross-cultural research teams use positionality and reflexivity to explicitly make choices about power dynamics in the context of executing partnership-driven work. This can be accomplished through personal and collective interrogation of the power dynamics at play in project administration, research questions, and interpersonal relationships.
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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 | 0.171 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.019 | 0.090 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.004 | 0.049 |
| Research integrity | 0.004 | 0.008 |
| 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".