Towards a Feminist Research Ethics of Care: Reflections, Lessons, and Methodological Considerations for Doing Research During a Pandemic
Bibliographic record
Abstract
Conducting feminist research during the global COVID-19 pandemic has evoked a renewed interest in the concept of care within our research team. The purpose of this paper is to provide concrete examples of how feminist ethics of care changed the initial and ongoing design of a community-engaged research project in Ontario, Canada. Drawing from examples and lessons learned, we focus on various adjustments to our methodological decision-making that intentionally honoured and prioritized our responsibilities to community partners, research participants, broader communities impacted by research, and the research team. By illustrating the methodological and ethical implications of these decisions, we argue that attending to these responsibilities prioritized both feminist research ethics and feminist ethics of care. As a result, we propose moving towards a conceptualization of a feminist research ethics of care, and discuss the value of this concept for researchers studying social justice.
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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 | 0.285 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.159 |
| Scholarly communication | 0.032 | 0.025 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.016 | 0.033 |
| 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".