Storytelling and co-authorship in feminist alliance work: reflections from a journey
Bibliographic record
Abstract
If all writing is fundamentally tied to the production of meanings and texts, then feminist research that blurs the borders of academia and activism is necessarily about the labor and politics of mobilizing experience for particular ends. Co-authoring stories is a chief tool by which feminists working in alliances across borders mobilize experience to write against relations of power that produce social violence, and to imagine and enact their own visions and ethics of social change. Such work demands a serious engagement with the complexities of identity, representation, and political imagination as well as a rethinking of the assumptions and possibilities associated with engagement and expertise. This article draws upon 16 years of partnership with activists in India and with academic co-authors in the USA to reflect on how storytelling across social, geographical, and institutional borders can enhance critical engagement with questions of violence and struggles for social change, while also troubling dominant discourses and methodologies inside and outside of the academy. In offering five ‘truths’ about co-authoring stories through alliance work, it reflects on the labor process, assumptions, possibilities, and risks associated with co-authorship as a tool for mobilizing intellectual spaces in which stories from multiple locations in an alliance can speak with one another and evolve into more nuanced critical interventions.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.031 | 0.053 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".