Using visual methods to capture embedded processes of resilience for youth across cultures and contexts.
Bibliographic record
Abstract
OBJECTIVES: We review the value of using visual data in a dialogue with youth, to reflect, explore and find language to better understand processes of resilience. METHODS: The argument is demonstrated with examples from the Negotiating Resilience Project (NRP): an international study of 16 youth which uses video recording a day in the life of youth participants, photographs produced by youth, and reflective interviews with the youth about their visual data. RESULTS: Three examples from the NRP are used to show the ways that visual methods can capture and elucidate previously hidden aspects of youth's positive psychosocial development in stressful social ecologies. CONCLUSION: Incorporating images as research data can aid in understanding previously unarticulated constructions of youth resilience. When the researcher is reflexive about power dynamics and their role in co-constructing the research environment, visual methods have the potential to reduce power imbalances in the field, meaningfully engage youth in the research process, and help to overcome language barriers.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".