Active Interview Tactics Revisited: A Multigenerational Perspective
Bibliographic record
Abstract
William (Billy) Shaffir taught about what it means to be a true empiricist, a sociologist committed to naturalistic observation as the most incisive method in our scientific toolbox. His inspiration still resonates, two decades later, in the work of new emerging scholars with the same commitment to ethnography—or what Billy more modestly and wisely calls “hanging around.” This paper is a tribute to his legacy that highlights the contributions of the next generation of graduate students that the lead author has been privileged to mentor at the University of Guelph. It builds on work by Hathaway and Atkinson on tactics of active interviewing to establish a more nuanced understanding of the benefits and challenges of being recognized as either an “insider” or “outsider,” and the implications of attempting to be both.
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 | 0.095 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.014 | 0.050 |
| Scholarly communication | 0.022 | 0.031 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".