“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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".