More than words: methods to elicit talk in interviews
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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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 | 0.060 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.010 |
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".