Reflexive approaches to sampling, survey design and implementation: some practical examples from rural India
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
Reflexivity in quantitative research is central to questioning the ways in which the data is designed, collected and interpreted. It requires the researcher to be reflexive about their knowledge, their positionality, and the bias they may bring at each stage of the research. Unfortunately, though, limited evidence exists on the use of reflexive approaches to adapt more ‘standardised’ approaches to quantitative research, such as sampling, design of survey instruments and the use of scales for measurement. Using the case of a large-scale data collection which took place in Uttar Pradesh, India, this paper demonstrates the challenges of sampling villages and selecting teachers and students as main sampling units. We further demonstrate the adaptation required with language and standard measure scales as well as how to consider enumerators as key stakeholders in the process of data collection. We argue that adaptions to standardised techniques are necessary to enhance the validity of quantitative research when working in diverse contexts.
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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 | 0.025 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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