Using PAR or Abusing its Good Name? the Challenges and Surprises of Photovoice and Film in a Study of Chronic Illness
Bibliographic record
Abstract
Without dispute, kidney dialysis treatment has been successful in saving lives. As a result of this intervention, increasing numbers of people are now facing the many physical, social, and emotional challenges of living with ESRD (end stage renal disease). Compromised vision, mobility, dexterity, and overall health have presented important methodological challenges to the authors' participatory action research (PAR) study of ESRD patients' quality of life. This article proceeds broadly in three steps: (a) an explanation of the authors' interest in PAR and the challenges that ESRD poses for PAR, (b) a description of how they adapted two visual techniques (photovoice and documentary film making) to address those challenges, and (c) a discussion of how they have and have not overcome the challenges of working with PAR.
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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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".