Bibliographic record
Abstract
BEYOND PROFESSIONAL AND INDIVIDUAL GOALS, what are we trying to do in sociology and why do we do it?Why is this work relevant for nonsociologists, for the "society?"These fundamental questions have been raised since the beginning of the discipline.However, in a world where sciences are contested, it is probably wise to come back to those questions in order to find relevant answers not only for ourselves and our students, but also for the skeptical ones.In this issue, we propose two sections of texts dealing with the issue of the relevance of sociology.The thematic section presents the views of B. Lahire, R. Connell, and O. Pyyhtinen.In the section Committing Sociology edited by Tracey Adams, we have six short texts coming from a Canadian symposium organized by A. Doucet and J. Siltanen in 2016.These texts are completed by one short presentation of the work of a colleague (K.Preibisch) who tried during her career to combine science and politics.On different topics, we also present two articles related to labor relations.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.165 | 0.155 |
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".