Doing Research Differently: Fostering Relational Ethics in Research Teams
Bibliographic record
Abstract
Feminist research methods have been historically collaborative and team-based, using the expertise of multiple researchers to analyze complex social problems. However, while feminist research teams often aim to be non-hierarchal and push against pre-existing power structures, they still exist within the neoliberal university which mediates how this resistance shows up. In this paper, we reflect on how a relational ethics of care can act as a disruptor to neoliberal academia. We offer reflections from a feminist research group on how we adopted a relational ethics of care and the impact it had on the research process. This article is not meant to be a guidebook for how to show care and resist neoliberal academic structure, but rather we hope to contribute to the ongoing conversations about how we can show up for each other and ourselves as researchers and beyond. We share this story in an attempt to foster a hopefulness within the messiness of relational research processes that have the potential to cultivate more compassionate and caring ways to conduct research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.021 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".