Methodological reflections on research with street youth
Bibliographic record
Abstract
Summary This paper examines both the epistemological and practical limitations and challenges of data collection by reflecting on the experiences of a team of both junior and senior researchers engaged in such a longitudinal study. Findings This paper argues that longitudinal research with street youth challenges the boundaries and limits of the formal constructs of research and ethics that typically guide qualitative research by grappling with field issues such as navigating reciprocity, risk and authenticity within relationships with a vulnerable group. Application This paper calls for an explicit acknowledgement of the challenges researching populations such as street youth over time in our research ethics guidelines and encourages researchers to engage in dialogue leading to more reflective, transparent and accountable framing of how we collect data in the field with vulnerable youth populations.
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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 | 0.191 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.024 | 0.041 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".