Bibliographic record
Abstract
Nearly five decades ago, Randall Collins (1975) wrote (and I paraphrase) that a sociology that does not recognize that people really only know their car, their driveway, the drive to work, and the people they work with, is not much of a sociology.Collins' insight-that the action was in the mundane everyday situations we found ourselves, where one gave orders generally or took them, was not new.It was in line with Goffman's approach, which was in line with the Chicago ethnographic approach that stretches back to Znaniecki, Thomas, Park, and so forth.It is in this tradition that Gary Alan Fine and Tim Hallett's work fits.The former, for several decades, has been the principal animating force behind a sociology of the mundane-or the local, as he prefers to call it-while the latter has expanded and extended these insights into a tantalizing examination of how group life inhabits and is inhabited by formal organizations.Many readers likely know this because of each authors' prolific bodies of scholarship.But, we also know that local sociology reveals something primordial to human sociality; that it rests somewhere near the center of the field, if not constituting the center; and yet, its familiarity sometimes gets hidden amidst whatever fads and fashions rule the day.And thus, their coauthored book, titled Group Life: An Invitation to Local Sociology, is simultaneously a full-throated call to adopt Fine's life-long methodological approach that has been built up and beyond by Hallett and a concise and well-written road map for setting out on this journey.To be sure, many of the street signs and rest stops on this road map may feel, intuitively, familiar, but for a local sociology the familiar is intentional, nay natural.Indeed, it is the underlying cornerstone of studying small groups in their inhabited environments.Fine and Hallett open their book with a poignant and pithy quote from James Joyce that captures why the familiar is so central."For myself," Joyce remarked, "I always write about Dublin, because if I can get to the heart of Dublin, I can get to the heart of all cities of the world" (as quoted on p. 1).The invitation to the local is an invitation to find and identify the universal in the most particular places.This may seem so obvious, but sociology spends an inordinate amount of time looking for
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.056 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".