The Lone Mother Resilience Project: A Qualitative Secondary Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although qualitative secondary analyses are conducted across the social sciences, supra-assorted analyses that involve both the re-use of existing data and the collection of new, primary data are relatively uncommon. Additionally, discussions regarding qualitative secondary analysis have tended to ignore the re-use of researchers' own data (i.e., auto-data). Thus, with this article, we aim to contribute to this discussion by providing an example of a supra-assorted analysis in which we re-used data from one of our previous studies, Lone Mothers: Building Social Inclusion. This earlier, longitudinal study was conducted with 104 poor lone mothers across Canada. We supplemented this dataset with data from three focus groups and 20 semi-structured interviews engaging a total of 38 lone mothers. Both studies were informed by a feminist and social inclusion lens, and recruited a diverse sample of women in three cities across the country: Vancouver, British Columbia; Toronto, Ontario; and St. John's, Newfoundland. In addition, most of the lone mothers who participated in the secondary analysis had also been involved in the original study as interviewees and/or research assistants. We conclude the article by discussing the strengths and limitations of, and lessons learned from, the secondary study's design.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.001 | 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