Bourdieu’s Rationalist Science of Science: Some Promises and Limitations
Bibliographic record
Abstract
At several points over his career, Pierre Bourdieu articulated a framework for a sociology of science, derived mostly from a priori reasoning about scientific actors in competition for capital. This article offers a brief overview of Bourdieu’s framework, placing it in the context of dominant trends in Science and Technology Studies. Bourdieu provides an excellent justification for the project of the sociology of science, and some starting points for analysis. However, his framework suffers from his commitment to a vague evolutionary epistemology, and from his correlative and surprising neglect of science’s habituses, with their particular practices, boundaries, and political economies. To be productive, Bourdieu’s sociology of science would have to abandon its narrow rationalism and embrace the material complexity of the sciences.
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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 | 0.052 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".