Wished I’d been there: Reflections on the special issue’s articles as refracted through my appreciations for qualitative researchers’ innovativeness
Bibliographic record
Abstract
Since the early 1990s, the innovativeness and critical responsiveness of qualitative researchers in studying psychotherapy and mental health has been astonishing to witness. A social constructionist or postmodern (e.g. with narrative, solution-focused, and collaborative approaches) therapist who went on to conduct, teach, and supervise qualitative research, I bring an appreciative view of this recent history to comment on the articles of this Special Issue. This history shows qualitative researchers developing and refining their methods to pose new questions and ways of answering them in line with therapist curiosities about meaning making. Qualitative research also became a tool for surfacing social justice concerns, or for answering or addressing questions in novel contexts. The innovativeness of qualitative researchers often derives from how new ideas, concerns, and sense-making practices are honed by methodical rigors that extend beyond the one-off understandings or hunches therapists develop through working with clients. Though use of any method has limitations on what can be claimed from its use, what matters most is that good qualitative research offers useful understandings. From this appreciation of qualitative research’s recent history and its potential usefulness to therapists and mental health workers, I selectively comment on the following articles.
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 | 0.043 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.021 | 0.041 |
| Insufficient payload (model declined to judge) | 0.005 | 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".