Bourdieu and Latour in STS : "Let's leave aside all the facts for a while"
Bibliographic record
Abstract
Through the lens of the English-speaking Science and Technology Studies (STS) community, the relationship between Pierre Bourdieu and Bruno Latour has remained semi-opaque. This thesis problematizes the Anglo understanding of the Bourdieu-Latour relationship and unsettles the resolve that maintains the distance that STS has kept from Bourdieu. Despite many similarities between these two scholars, Bourdieu has remained a distant figure to STS despite his predominance in disciplines from which STS frequently borrows and the relevance of his corpus to topics dear to the heart of STS. This is in part due to Latour's frequent criticisms of Bourdieu by name, Latour’s philosophical disagreements with Kant and neoKantians, and Latour’s prestige in STS, and partially due to Bourdieu’s somewhat indirect or orthogonal ways of addressing natural and physical sciences and technology. Due to the fact that the writings of both needed to be translated from the original French to be received by Anglo audiences, important cultural, stylistic, and rhetorical nuances were lost, mistranslated, or not translated across the linguistic and geographical divides. Including these distinctions is invaluable to understanding their relationship and further weakens the justification for Bourdieu's absence from STS.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".