Bibliographic record
Abstract
THE WRITING of acknowledgments marks the true end of a project.It provides an occasion to repress the difficulties, to remember the moments of ecstasy, and to express appreciation for the good deeds that made it all possible.First, I want to thank the 150 men who were interviewed for this book.They were trusting enough to tell me how they see things, and I hope that my analysis does not betray them.Their remarkable openness helped me to understand the conditions underlying their views.Many also showed great humanity, which I found very inspiring.Without people like these men, qualitative sociology would simply not exist and I would be deprived of one of the pleasures of my life.For all this, thanks!This research began in 1992 with small grants from Princeton University, namely from the Committee for Research in the Social Sciences and the Humanities, the Center for Excellence in French Studies, and the Center for International Studies of the Woodrow Wilson School.These grants sowed the seeds that resulted in a generous grant from the National Science Foundation, which covered the research expenses of this project.A fellowship from the German Marshall Funds of the United States freed me to conduct interviews.During this period, I benefited from the hospitality of the Schomburg Center for the Study of Black Culture at the New York Public Library, the Institute for French Studies of New York University, and the Centre de sociologie politique et morale at the Ecole des hautes etudes en science sociales in Paris.Most of the book was written when I was on leave with the support of fellowships from the Russell Sage Foundation and the John Simon Guggenheim Memorial Foundation.I spent the fall of 1996 at the Russell Sage Foundation, where, like many generations of scholars, I found
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.221 | 0.120 |
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".