The more you learn the less you know? Interpretive ambiguity across three modes of qualitative data
Bibliographic record
Abstract
BACKGROUND: Researchers across disciplines face a similar challenge ensuring our methods can give us valid, usable answers to our questions. But what happens when multiple strategies of inquiry give us different answers to the same research question? We explore this question through three different modes of qualitative inquiry--interviews, focus groups, and participant observation - oriented around local attitudes to HIV testing. OBJECTIVE: We introduce the notion of "research awareness" -- the extent to which participants are continuously reminded that they are taking part in a research project, which is a function of the mode of research itself. We hypothesize that as participants' research-awareness decreases across modes, from interviews to focus groups to participant observation, the proportion of statements that conform to officially sanctioned normative discourse about HIV/AIDS will decrease and the proportion expressing non-normative or counter-normative ideas will increase. METHODS: We tabulated positive and negative references to three themes -- knowing one's HIV status, counseling messages, and antiretroviral treatment -- across the three qualitative modes. RESULTS: The distribution is non-uniform, with favorable responses to testing themes predominating in interviews, mixed responses in the focus groups, and negative responses predominating in the observational data. At least 1/3 of references to testing across all three modes, however, do not support officially sanctioned normative discourse. CONCLUSIONS: Researchers who use mixed methods approaches for triangulation should consider the influence of research-awareness on their methods. These situational specifics are crucial for understanding the applicability of research to real life. Substantively, our study revealed a robust level of ambivalence about HIV testing despite normative discourses supporting it at local and global levels.
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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 | 0.367 | 0.436 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| 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; 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".