<i>‘I have the confidence to ask’</i>: thickening agency among adolescent girls in Karnataka, South India
Bibliographic record
Abstract
Gender norms serve to normalise gender inequalities and constrain girls’ agency. This paper examines how girls’ agency, along a continuum, is influenced by the interplay between constraining and enabling influences in the girls’ environments. We analyse data from a qualitative study nested within a cluster randomised evaluation of Samata, a multi-layered programme supporting adolescent girls to stay in school and delay marriage in Karnataka, South India. Specifically, we compare agency among 22 girls from intervention communities and 9 girls in control communities using data from the final round of interviews in a qualitative cohort. Using the concept of ‘thin’ and ‘thick’ agency on a continuum, we identified shocks like mothers’ death or illness, poverty stress, gender norms and poor school performance as thinning influences. Good school examination results; norms in support of education; established educational aspirations; supportive parents, siblings and teachers; and strategic government and Samata resources enabled thicker agency. The intervention programme’s effect increased in parallel to the gradient from thin to thicker agency among girls in progressively supportive family contexts. Engagement with the programme was however selective; families adhering to harmful gender norms were not receptive to outreach. In line with diffusion theory, late adopters required additional peer encouragement to change norms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".