The Epistemological and Ethical Value of Autophotography for Mobilities Research in Transcultural Contexts
Bibliographic record
Abstract
This article responds to calls from mobilities scholars for methodological innovation and reflexivity by (a) detailing our use of autophotography in a study of the everyday implications of a newly-constructed road for a small community in mountainous northern Pakistan, and (b) assessing autophotography’s attributes as a visual/narrative method for mobilities research in that setting, on ethical and epistemological grounds. We demonstrate that autophotography’s anti-objectivist epistemology of vision and participant-driven character, the portability and easy user-interface of compact cameras, and the inseparable mix of visual and narrative data the method produces, combined to attenuate epistemic injustice in our research, while also generating productive insights regarding the movements, representations and embodied practices our research subjects associate with the road. These points are developed with reference to literature on visual methods, mobile methods and subaltern autoethnography, as well as to the visual/narrative representations produced by study participants. The article concludes by exemplifying how research subjects used the road and its associated mobilities as discursive resources for the constitution of collective identity: to position their community in relation to modernity and tradition, to distinguish the community from its neighbours, and to articulate worries about the consequences of rapid social change.
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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 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.117 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".