Citation Elites in Polytheistic and Umbrella Disciplines: Patterns of Stratification and Concentration in Danish and British Science
Bibliographic record
Abstract
Abstract The notion of science as a stratified system is clearly manifested in the markedly uneven distribution of productivity, rewards, resources, and recognition. Although previous studies have shown that institutional environments for conducting research differ significantly between national science systems, disciplines, and subfields, it remains to be shown whether any systematic variations and patterns in inequalities exist among researchers in different national and domain specific settings. This study investigates the positioning of citation elites as opposed to ‘ordinary’ researchers by way of examining three dimensions of concentration (accumulation of publications and citations, specialisation, and institutional concentration) in biology, economics and physics in Denmark and the UK. Across all three dimensions, we put Richard Whitley’s bipartite theory to the test, suggesting a nexus between the intellectual structure of a discipline and the configuration of its elite. The study draws on a dataset of researchers who published most of their publications in either physics, biology, or economics over the 1980–2018 period and with at least one publication in 2017–2018 while affiliated to either a British or a Danish university. We find higher degrees of concentration in the UK compared to Denmark, and that physics and biology respectively display the greatest and lowest degree of concentration. Similar patterns in disciplinary differences are observed in both countries, suggesting that concentration patterns are largely rooted in disciplinary cultures and merely amplified by the national context.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".