Reflections on Using Participant-Generated, Digital Photo-Elicitation in Research With Young Canadians About Their First Part-Time Jobs
Bibliographic record
Abstract
Participant-generated photo-elicitation usually involves inviting participants to take photographs, which are then discussed during a subsequent interview or in a focus group. This approach can provide participants with the opportunity to bring their own content and interests into research. Following other child and youth researchers, we were drawn to the potential of participant-generated photo-elicitation to offer a methodological counterweight to existing inequalities between adult researchers and younger participants. In this article, we reflect on our use of one-on-one, participant-generated photo-elicitation interviews in a Canadian-based research project looking at young people’s earliest paid work. We discuss some of the challenges faced when it came to gaining institutional ethics approval and also report on how the method was unexpectedly but productively altered by participants’ use of publicly accessible Internet images to convey aspects of their work. Overall, we conclude that participant-generated photo-elicitation democratized the research process and deepened our insights into young people’s early work and offer some recommendations for future photo-elicitation research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.073 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.065 | 0.030 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".