Bibliographic record
Abstract
Some brilliant images are projected from Juliet CORBIN's memories around her first steps into the qualita- tive research world related to the symbolic interactionism tradition. She focuses on some remarkable issues about learning the processes of Grounded Theory based on her past experiences teaching in seminars or doing workshops worldwide. The differences between writing novels and the narrative perspective and writing social science from Grounded Theory methodology are discussed by her in or- der to distinguish the role of literature in the production of socially active knowledge. About the Interview: I e-mailed Juliet CORBIN about interviewing prominent qualitative researchers for FQS. She agreed and granted permission for the interview. We met twice for the interview with the focus of the sessions being on her current projects and her personal experience of becoming a qualitative researcher. Our two meetings happened to take place at two large qualitative research conferences we were both attending as presenters, one conference in North America and the other conference being held in South America. I met Juliet CORBIN for the first time when she was in Guadalajara, Mexico in the year 2000 conducting a Grounded Theory workshop. The work- shop provided an opportunity for all in attendance to learn more about this research tradition and to have our questions about this particular method an- swered. The workshop was supported by the International Institute for Qualita- tive Methodology, University of Alberta, and its international site located in Guadalajara, Mexico at the University of Guadalajara. In and around the work-
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.021 | 0.018 |
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".