Balancing Consistency and Flexibility: Challenges and Opportunities in Conducting a Cross-Country Longitudinal Study with Youth Participants in Work-Integration Social Enterprises
Bibliographic record
Abstract
Longitudinal studies conducted within the social economy have the potential to provide useful insights by tracing participant experiences and illuminating long-term outcomes of program interventions. However, longitudinal studies are challenging, not only due to retention of participants, but also when a longitudinal study covers a broad geographic area. The authors collaborated in a five-year pan-Canadian longitudinal study following youth participants in work-integration social enterprise (WISE) training programs. This article traces the experiences of study teams in Ontario and Greater Vancouver, providing accounts of the approaches and challenges encountered when working in geographically and socio-economically diverse locales over time with youth participants facing social marginalization. This article highlights three aspects of data collection—recruitment, retention, and research methods and logistics—offering insights into how each team devised its own strategies to fit with local circumstances while maintaining consistency across research sites.
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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 | 0.405 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".