Bibliographic record
Abstract
Using PhotoVoice, a participatory visual methodology, this research explored the settlement experiences of refugee youth who have exited high school and a program designed by a school board in Alberta to support their language and academic needs. Newcomer youth encounter profound academic and social stresses as they attempt to create a new identity and sense of belonging in their new home. By engaging the notion of place as a framework, this project examined what it means for refugee youth to recuperate a place of belonging. Gruenewald (2003) suggests that understanding our relationship to place can be profoundly pedagogical. The youth began by capturing their perspectives on belonging with photos; they collaboratively analyzed them for common themes, audio recorded narratives to accompany key images and then shared this assembled digital product with recently arrived newcomer youth. By focusing on the notion of belonging rather than barriers to settlement, the youth reflected on the actions that were instrumental in their effort to inhabit their new home. The findings revealed that at the core of the youth’s efforts, connecting to people, especially in school, in addition to connecting to the natural world, fostered feelings of well-being and belonging. Educational implications include recommendations for schools and teachers supporting newcomer youth. Schools that offer welcoming and focused language programs with teachers who have trauma sensitive training provide a foundation for older newcomer youth. Meaningful relationships among teachers, students and families generate trust that in turn creates safe places for student needs and voices to be understood. Giving experienced youth opportunities to reflect on and share their perspectives with other youth fosters confidence and awareness for both groups.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| 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".