Homologies in fields of cultural production. Evidence from the European scientific field
Bibliographic record
Abstract
• Homology refers to general principles of vision and division. • These principles are at play both within and across fields. • Scientific disciplines are considered as fields of action. • Mapping topic spaces is helpful to address homology. • Scientific fields display strong yet varying homology in their relational structure. This article suggests a comparative field analytical approach to fields of cultural production. Combining concepts from field analysis and focusing on homology with topic modeling and multiple correspondence analysis, we compare four scientific disciplines and show homological structures along both internal and external principles of differentiation. The empirical analysis suggests that despite major differences between the four disciplines (biology, chemistry, economics, and sociology), they are structured along similar principles. Moreover, cognitive distinctions in certain disciplines can be correlated with institutional properties and symbolic hierarchies. Despite the similarities, the analysis also shows important differences between the four disciplines related to internal organization and their relations to both other scientific disciplines and the field of power. The article shows how topic modeling and multiple correspondence analysis can cross-fertilize to understand how fields of cultural production differentiate and how cultural practices (here scientific knowledge production) relate to social structures (here academic hierarchies and prestige). The method hence allows for comparison between fields of cultural production while retaining a nuanced analysis of specific fields and the practices that constitute them.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".