Timeline Mapping in Qualitative Interviews: A Study of Resilience with Marginalized Groups
Why this work is in the frame
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Bibliographic record
Abstract
Growing interest in visual timeline methods signals a need for critical engagement. Drawing on critical emancipatory epistemologies in our study exploring resilience among marginalized groups, we investigate how the creation of visual timelines informs verbal semistructured interviewing. We consider both how experiences of drawing timelines and how the role of the timeline in interviews varied for South Asian immigrant women who experienced domestic violence, and street-involved youth who experienced prior or recent violent victimization. Here we focus on three overarching themes developed through analysis of timelines: (a) rapport building, (b) participants as navigators, and (c) therapeutic moments and positive closure. In the discussion, we engage with the potential of visual timelines to supplement and situate semistructured interviewing, and illustrate how the framing of research is central to whether that research maintains a critical emancipatory orientation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.121 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it