“You Have Some Questions for Me?” considering Qualitative Interviewing
Bibliographic record
Abstract
Interviewing, as a way of collecting research data, has emerged and been developed within the transition from modernist ideals to more postmodern perspectives about what constitutes knowledge. Interviewing practices range from standardized structured interviews to collect data in large-scale studies, to interviews that are more characteristic of a conversation that allow for a more expansive venture into an area of inquiry. While there are times when ideas develop over time and one can see the evolution of them, this is not as clear with the ideas of interviewing. In this state-of-the-art article, we survey the fields where interviews are visible and see the presence of different forms of interviews. Research interviews necessitate recognition of the ontological and epistemological commitments that shape research study design. A research interview cannot be crafted without attending to questions about: the purpose of the interview; the place it takes up alongside the needs, interests, and vulnerabilities of researchers and participants; the relationship between the intent for interviews and the places in which research interviews are conducted; how research interviews can open up as well as foreclose insight into experiences; and, how interviews are shaped by considerations of power and positionality. By attending to these questions, researchers can resist reducing interviews to a simplified ask-and-answer procedure.
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How this classification was reachedexpand
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 | 0.114 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".