Portraits of pain and promise: a photographic study of Bosnian youth.
Bibliographic record
Abstract
In the early 1990s, war erupted in Bosnia and Herzegovina, forcing large numbers of people to flee their homes and country, abandoning their culture and all that was familiar to them. For the children, often described as war's "innocent victims," the conflict and subsequent uprooting represented a dramatic end to their peaceful lives. Although many were fortunate enough to escape with their families and resettle amid more peaceful circumstances, there is considerable evidence that refugee youth are forever changed by their exposure to war and that the pain of war does not end when the fighting is over. This paper presents the results of a study with 7 Bosnian children, aged 11-14, who came to Canada as refugees during the 1990s. The everyday challenges and struggles faced by this group were explored using an innovative research method called photo novella. A secondary purpose of the research was to evaluate the merits and limitations of photo novella as a method for capturing children's perspectives and feelings. Participants were given disposable cameras and asked to take pictures of important people, places, and events. The meaning of the photographs was then explored through a dialogic process the researchers call phototalk. The findings revealed that while these children had many strengths, they continued to struggle to understand the events that so profoundly changed their lives. The results and the implications for nurses are discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".