Relational Ethics of Care in Pandemic Research: Vulnerabilities, Intimacies, and Becoming Together-Apart
Bibliographic record
Abstract
In this article, we draw upon the ethico-onto-epistemology of feminist new materialisms to reflect on our experiences as feminists doing research on women’s embodied experiences of sport, fitness, and well-being during the COVID-19 pandemic. For qualitative researchers around the world, COVID-19 presented a radically changed research environment. For many, the shift to doing digital interviews required the navigation of unfamiliar technologies and experimenting with different strategies for establishing connections through computer screens. As feminist scholars, working together and with the participants during times of increased stress and uncertainty prompted us to reimagine our ethical research practices. In this article, we engage and extend Rosi Braidotti’s writing on affirmative ethics and offer our personal experiences of grappling with the affective intensities of pandemic while doing ethical feminist research. Through this creative inquiry, we describe supporting one another through research and illustrate how the unique intersections of work, family, health, isolation, and exhaustion were influencing our own and participants’ lives differently. Engaging with Braidotti’s writings on affirmative ethics in the posthuman convergence, we illuminate the ways that our digital-material experiences and the human/nonhuman aspects of the research processes were re-turning our ethical considerations. Researching together, with a focus on creating space for the voices of women who have been disproportionately affected by COVID-19, we found moments of hope and joy as we creatively imagined expansive potentials for feminist research, fostered through caring collaborations.
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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 | 0.116 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.202 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.010 | 0.018 |
| 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".