Me and my cellphone: constructing change from the inside through cellphilms and participatory video in a rural community
Bibliographic record
Abstract
Drawing on fieldwork with rural teachers inSouthAfrica, this article highlights the significance of cellphone technology in participatory video and its potential to alter the research environment. To date much of the work in the area of participatory visual methodologies (including participatory video) and particularly in the context of working with marginalized communities, has relied on researcher‐led projects wherein it is the research team who as outsiders bring cameras for research with the community. In most cases the team departs, taking the cameras with them, but even in cases where the video cameras are left behind, sustainability is still an issue. In the case of cellphones and the production of cellphilms, the dynamics change. We reflect on our fieldwork in two rural schools, where all of the teachers had cellphones and regularly used them for various forms of communication including texting and accessingFacebook. None however, prior to the project had ever produced cellphilms, and only one had used a cellphone in any pedagogical way. In considering critical issues of using participatory video, we addressTouraine andDuff's (1981The voice and the eye: an analysis of social movements CambridgeUniversityPress,Cambridge) notion of the sociological intervention, and ask questions such as: Can this work with cellphones be regarded as a non‐interventionist intervention? How does the widespread use of cellphone technology alter the power dynamics related to ownership of both the production and the recording device? To what extent do some of the ethical concerns of previous work become obsolete and to what extent are there new ethical concerns (for example, distribution) to be addressed?
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.029 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".