Bibliographic record
Abstract
To write a sociological festschrift for a scholar necessarily means looking at a chain of influence instead of one person. In this essay, I honor William Shaffir, Emeritus Professor of Sociology at McMaster University, who taught me as I worked towards the MA. I examine what I learned from him by starting with my undergraduate experiences at McGill University, where Billy (I never heard anyone call him William) received his PhD. We shared influences there, including those who had studied with Howard S. Becker at Northwestern University. I then turn to my time at McMaster, and how Billy strengthened my knowledge of symbolic interactionism and qualitative methods, as well as taught me important lessons about writing. He also reduced graduate students’ anxieties, including mine, through two words: “No problem.” My experiences with Billy provided a model of mentoring that challenged the usual hierarchy between graduate students and professors. Those lessons were reinforced as I pursued a PhD at the University of Minnesota and spent two quarters at Northwestern University as a visiting student. These connecting influences helped me write and teach sociology in a largely quantitative department at the University of North Carolina-Chapel Hill, where I lacked the kind of support I had received as an undergraduate and graduate student. I taught there over 37 years, practicing the kind of sociology and mentoring that Billy generously modeled so many years ago.
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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 | 0.028 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".