A Multi-Stage Approach to Qualitative Sampling within a Mixed Methods Evaluation: Some Reflections on Purpose and Process
Bibliographic record
Abstract
Abstract: We share experiences from a mixed methods evaluation in rural India that combines a randomized controlled trial (RCT) of 400 villages with embedded case studies in four villages. Specifically, we present two lessons from the multi-stage sampling approach adopted to select the four case-study villages, which first prioritized key-informant observations regarding intervention status in order to shortlist locations and subsequently used data from the RCT’s baseline survey to select the final sample. In doing so, we highlight how large-scale mixed methods program evaluations in education can go beyond questions of “what works” to answering those of “how,” “why,” and “why not.”
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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 | 0.603 | 0.399 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.067 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.011 | 0.023 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".