Bibliographic record
Abstract
Purpose Despite the growing pressure to encourage new ways of thinking about research methodology, only recently have interview methodologists begun to realize that “we cannot lift the results of interviewing out of the contexts in which they were gathered and claim them as objective data with no strings attached”. The purpose of this paper is to provide additional insight based on a critical reflection of the interview as a research method drawing upon Alvesson's discussion from the neopositivist, romanticist and localist interview perspectives. Specifically, the authors focus on critical reflections of three broad categories of a continuum of interview methods: structured, semi‐structured and unstructured interviews. Design/methodology/approach The authors adopt a critical and reflexive approach to understanding the literature on interviews to develop alternative insights about the use of interviews as a qualitative research method. Findings After examining the neopositivist (interview as a “tool”) and romanticist (interview as “human encounter”) perspectives on the use of the research interview, the authors adopt a localist perspective towards interviews and argue that the localist approach opens up alternative understanding of the interview process and the accounts produced provide additional insights. The insights are used to outline the skills researchers need to develop in applying the localist perspective to interviews. Originality/value The paper provides an alternative perspective on the practice of conducting interviews, recognizing interviews as complex social and organizational phenomena rather than just a research method.
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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 | 0.050 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.021 |
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".