Imagining the Other? Ethical Challenges of Researching and Writing Women's Embodied Lives
Bibliographic record
Abstract
Feminists influenced by post-conventional and critical perspectives confront a significant challenge when researching women's embodiments: the dilemma of representation. For researchers from positions of bodily privilege, issues of interpretation intensify when researching and writing across physical differences distorted by colonial and other hegemonic histories and legacies. In this article, I draw from interviews with diversely embodied women to discuss difficulties encountered in interpreting their narratives of embodiment. I reflect on strategies of embodied engagement, including de-centring my bodily self, re-visiting my body story, and imagining the other's embodied experiences in the creation of provisional meanings about participants' bodies and lives. To shed light on risks and rewards of researcher-embodied reflexivity to study sensitive subjects such as appearance and difference, I show how analysing my `body secrets' invites deeper exploration into dynamics of bodily privilege and abjection underpinning women's accounts. I conclude by questioning the ethics of my `imaginative leap' into other/ed women's lives and by considering more broadly the perils and possibilities of traversing the space between self and other, and other in the self, within feminist research.
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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 | 0.127 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.120 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.011 |
| 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".