The relative (in)visibility of sociologists in the French, American, British, and German national fields (1970–2018)
Bibliographic record
Abstract
In this article, we analyze the relative (in)visibility of authors in four countries that are central to the global production of sociology: France, the United States, the United Kingdom, and Germany. A cross-comparison of these national fields shows that, although citation distributions consistently follow a power law, authors who are the most frequently cited in their national field are not necessarily those who are the most frequently cited abroad. Mapping the space of national and international visibility of authors in each analyzed country shows an invariant structure: a majority of authors are only visible nationally, fewer authors reach a large national visibility and relative international visibility, and even fewer authors reach a large national and international visibility. Thus, only a very few authors of the four countries achieve what we can call a ‘global’ visibility, which is associated with the production and circulation, through translations, of works of theoretical nature. Our findings generalize Etienne Ollion and Andrew Abbott’s analysis of the reception of French sociologists in the field of American sociology, by showing that their results have nothing specific to the French case and rather constitute a very general result that applies to the distribution of citations within any national field.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".