Bibliographic record
Abstract
In qualitative research (QR), teaching is essential to the production and reproduction of knowledge both substantively and institutionally. Despite this, teaching QR has not received much scholarly attention. This Special Issue will address this problem by conceptualizing the teaching of QR as involving a set of transgressive practices that sustain and realize critical perspectives and practices in QR. It emphasizes that the teaching of QR is context specific and that what and how to teach need to be interrogated. The Special Issue includes two clusters of articles: (a) four full-length articles contributed by qualitative practitioners from the geopolitical South, aboriginal scholars within the Western core, and scholars in “scientific” fields in the core. It also includes (b) five shorter articles that address key concepts and/or principles of QR. As a catalyst, the Special Issue facilitates discussion and disrupts existing domination along the core–periphery, Aboriginal–non-Aboriginal, and science–social sciences divides.
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 | 0.302 | 0.287 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier 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".