Bibliographic record
Abstract
Today, 38 years after receiving my doctorate in sociology from the University of Minnesota, I describe myself, in general, as a qualitative-exploratory researcher and, in particular, as a specialist in French Canada, the sociology of leisure, and the sociology of deviance. Looking back on my Minnesota origins, nothing I can see there would have led me or anyone else to predict a scholarly career composed of these elements. In the early 1960s, when I was a graduate student, the Minnesota Department seemed bent on doing its best to live up to that University’s reputation as world home of “dustbowl empiricism. ” This methodological orientation, highly quantitative and predictive as it was, was nevertheless quite primitive by today’s standards. But certainly no one spoke of qualitative or exploratory research, although Glaser and Strauss (1967: 15-18) had observed about the same time that the qualitative distinction had become ever more current between the late 1930s and the early 1950s when quantification and measurement were coming into their own As for my interest in French Canada, the Minnesota Department can hardly be blamed for failing to nurture an attraction to this specialty, but I, as an individual, might be blamed for failing to follow immediately the lead of Gregory P. Stone in pursuing a career in the sociology of
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".