Young Lives qualitative research: round 1 – India: Young Lives Technical Note 21
Bibliographic record
Abstract
This report presents an initial analysis of some of the qualitative data collected in four of the 20 Young Lives sites in Andhra Pradesh during October to November 2007 (‘Qual-1’). The sub-sample was drawn from both cohorts of Young Lives children – the Younger Cohort (aged 6 to 7) and the Older Cohort (aged 12 to 13) – as well as their caregivers, teachers, health workers and community representatives. \n\n The sub-sample includes 48 children, 12 from each of the sites, with equal numbers of boys and girls from each cohort. Further key variables for sub-sampling included caste, parental presence, school enrolment, pre-school attendance and type of school attended. These criteria were used to select a core group of ‘case study’ children, in addition to another eight children per community who could replace these children if they subsequently dropped out; the latter were also included in group-based research activities.Three overriding questions guided the qualitative research: \n\n What are the key transitions in children’s lives, how are they experienced (particularly in relation to activities, relationships, identities and well-being) and what influences these experiences? \n How is children’s well-being understood and evaluated by children, caregivers and other stakeholders? \n How do policies, programmes and services shape children’s transitions and wellbeing? \n\n Research into these questions aimed to be sensitive to both differences between children (for example, age, gender, socio-economic status, and ethnic, linguistic and religious identity), and inter-generational differences (for example, in the perspectives of children and their caregivers). The qualitative research used a mix of methods to generate data on these themes, including individual interviews with children (both cohorts), caregivers and other key stakeholders – e.g., pre-school, primary and high school teachers, health workers and the village head (sarpanch) – and group interviews with adults in the community. Creative methods using drawing, mapping and neighbourhood walks with children were also introduced. Semi-structured observations of homes, schools and community settings provided the context for analysing and understanding the data.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".