Theorizing Qualitative Research Interviews in Applied Linguistics
Bibliographic record
Abstract
Interviews have long been used as a method in applied linguistics for the investigation of an extraordinary array of phenomena. In quantitative research, interviews have been used to generate insights into matters as varied as cognitive processes in language learning, lexical inferencing, motivation, language attitudes, program evaluation, language classroom pedagogy, language proficiency, and learner autonomy (see e.g. Brown 1988; Dörnyei 2007; Gass and Mackey 2007). In qualitative research, interviews have featured in ethnographies, case studies, and action research concerning an equally diverse array of topics, as well as narrative inquiries, (auto)biographical research, and, of course, interview studies, which investigate participants’ identities, experiences, beliefs, life histories, and more (see e.g. Burnaby and Sun 1989; Simon-Maeda 2004; Barkuizen 2009). In fact, given the recent shift away from paradigm wars to mixed methods research (Bryman 2006; Creswell 2009; Denzin 2010), we can in the future expect to see interviews feature across an even broader range of studies.
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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 | 0.088 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".