PHOTO-NARRATIVE PROCESSES WITH CHILDREN AND YOUNG PEOPLE
Bibliographic record
Abstract
This article focuses on the photo-narrative research process with children and young people. The photo-narrative method invites children and young people to answer research questions by first taking photographs and then talking to the researcher about them. We reflect critically on our own photo-narrative study by asking such questions as: In what ways can the photo-narrative method be seen as a participative method? How were the various power relations between the child and the researcher actualized? What methodological and ethical challenges did we encounter during the research process? The study data were photographs and narratives by eight children and young people (aged 4 to 15 years), who were each interviewed twice. In the first interview, each participant was given a disposable camera and they were asked to take photographs of things and situations, persons, objects, and feelings relating to their everyday lives during one week. The second interview was a narrative interview where each participant could select the photographs he or she wanted to talk about. In this approach, interpretation of the photographs was primarily in the hands of the children and young people, while interpretation of the narratives was the responsibility of the researcher.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".