More than words: methods to elicit talk in interviews
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
Lay Summary In health services and primary care research, semi-structured interviews are a very common method of generating data. These interviews have a pre-determined set of topics, with questions and prompts written in advance, though there is flexibility to adjust the interview to match the direction set by the participant. Like all methods, semi-structured interviews have limits, some of which can be addressed through adaptation. In the social sciences, some interview methods include prompts beyond verbal questions to participants, called elicitation tools. Visuals (e.g. photos), videos, audio excerpts and texts can be brought into interviews to orient the discussion. Another type of interview—mobile interview—happens in places meaningful to the participants. Depending on the research question, elicitation methods can enrich semi-structured interviews. This methods brief will introduce interviewing with elicitation tools, and outline strengths of such methods.
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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 | 0.019 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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