Bibliographic record
Abstract
Abstract This chapter examines the research team of sociologists that, beginning in 1896, collaborated with Émile Durkheim to create the journal L’Année sociologique. It explores the central place that Durkheim held in the group, as well as the vital roles that different collaborators such as Célestin Bouglé and Marcel Mauss played in making L’Année sociologique an initial success. The chapter then follows the development of this Durkheimian school and its historical legacy after Durkheim’s death in 1917. This development includes the Durkheimian school’s maintenance of a prominent position in the 1920s and 1930s, its relative post–World War II obscurity, and its rebirth beginning in the 1970s and 1980s through renewed academic interest in the work of members of the team. Beyond L’Année sociologique, special attention is given to specific members of Durkheim’s team, including collaborators such as Henri Hubert, François Simiand, Maurice Halbwachs, and Robert Hertz.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".