‘Is this for real?’ Participatory research, intersectionality, and the development of leader and collective efficacy with young mothers
Bibliographic record
Abstract
Young mothers are often socially and economically marginalized, and tend to be discussed as one homogenous, high-risk, deficit-based group. They are frequently excluded from youth participatory action research and youth leadership research, which leaves little space for considering them as engaged citizens or leaders. To contribute to filling this gap, I created and implemented, in collaboration with a group of 11 young mothers, a policy-focused participatory research project called Engage for Change. My objectives included generating youth-based research evidence for informing public policy; exploring intersectionality as a theoretical foundation for participatory research with young mothers; and better understanding the impacts of participatory research on young mothers’ leader and collective efficacy. In this article, I describe the implementation of Engage for Change, highlighting the work of participating young mothers. I explore intersectionality as a theoretical foundation, and consider the leader and collective efficacy development of participants, with the goal of: 1) contributing to the sparse literature on participatory research with young mothers; 2) suggesting that intersectionality is a useful theoretical foundation for participatory research with young mothers; and 3) highlighting participants’ leader and collective efficacy, while acknowledging persistent systemic exclusions they face in exercising leadership and engagement.
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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 | 0.048 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".