Bibliographic record
Abstract
analytical lens of the book on the mesolevel helps us make sense of social action and engagement that is not organized by state actors or other top-down processes.Consequently, the book is good (and fun) to think with about a range of important forms of action.For instance, Fine observes that the widespread practice of holidays is sustained across time and place not only through affordances of institutions that provide days off and through consumer capitalism scaffolding that supports them, but also by being rooted in communities of people who regularly gather, and who have developed sets of shared practices and meanings around those holidays is valuable.This helps suggest a different pathway to making holidays like Martin Luther King Jr. Day more robustly celebrated; days of service that are not rooted in specific community and groups may be less effective than those that are rooted in and attentive to group life.Fine also provides some nuanced thinking about the relationship between narratives and group life that has been largely concentrated within the literature on social movements, but which gains a broader appeal in his treatment.The ideas in this book prompted a range of stimulating conversations with academics and non-academics alike as I read it.And, it opens the door to an abundance of empirical social scientific inquiries into profoundly important issues.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.074 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".