(En)tangled Ethics and Relational Mobilities: Reflections on Decolonial Feminist Digital Participatory Action Research
Bibliographic record
Abstract
This paper explores the ethical entanglements and tensions that arise when using a decolonial feminist approach to digital participatory action research (DPAR) within the context of bicycling, mobilit(ies), and gender justice in rural Nicaragua. Grounded in a reflexive feminist ethics of care and relational mobilities, I reflect on a collaborative DPAR project with co-researchers that aimed to address the intertwined issues of mobility justice, sexual and gender-based violence (SGBV) and climate change. Through digital methods - such as photovoice and participatory GIS mapping using GoPro cameras - co-researchers mapped specific community spaces marked by SGBV and environmental precarity, revealing the layered dynamics of gendered, racialized and ecological injustice that shaped their everyday mobilities. Drawing on these experiences, I offer critical reflections on the possibilities and tensions of decolonial feminist DPAR, including (1) the necessity of a feminist reflexive ethics of care; and (2) the ethical entanglements involved in using technological interventions that may inadvertently reproduce structural inequalities. While digital technologies may pry open opportunities for collective storytelling and community advocacy, I contend that their use must be guided by ongoing ethical considerations to ensure that relational mobilities and accountabilities - and the voices of co-researchers in DPAR projects - remain central.
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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 | 0.049 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.111 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".