Picturing the researcher: Using photovoice to document the research assistant experience during the COVID-19 pandemic
Bibliographic record
Abstract
The article is a reflection by two graduate research assistants (GRAs) who experienced the effects of the COVID-19 pandemic on the in-person interactions through which qualitative researchers usually learn about human experiences. With in-person research curtailed, the authors were compelled to think creatively and find other ways to continue their research and develop meaning. The researchers reflected on their experiences as GRAs for the study ‘Thriving in Canada: Learning from the (photo) voices of women living on a low income engaged in action research to improve access to health and social services’. Taking advantage of pandemic-related study delays, the researchers explored the photovoice method in more depth and used photovoice to document their own lived experience as GRAs, and their learning. They practised self-reflexivity and worked to improve their visual-based photovoice facilitation skills. This illustrated essay is the story of the authors’ experiences over the past year working as GRAs during the COVID-19 global pandemic.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".