Too local relative to what? Cross-national and cross-disciplinary variability in the tension between local and cosmopolitan coverage in nation-branded social science journals
Bibliographic record
Abstract
In the social sciences, certain academic journals brand themselves as local to some geographical nation – ‘American’, ‘British’, ‘Canadian’, or ‘Chinese’. However, some of these journals aim to publish research not only about the domestic society indicated in their name but also research about another individual society, multiple societies, or society in general. Despite this editorial aspiration, for over 60 years, scholars have regularly critiqued the ‘American’ branded journals as being overly ‘ethnocentric’, ‘parochial’, or ‘provincial’ – in a word, too local and not truly cosmopolitan. Yet hardly any of these critiques systematically compares how local ‘American’ journals are compared to other nation-branded journals. In this article, I conduct a content analysis of all articles published in nation-branded political science and sociology journals from 2019 until 2023 to show what percentage their articles each year are about their local society, how many are about a single foreign society, and how much are about multiple societies or society in general. I show how ‘Canadian’ and ‘Chinese’ branded journals are more locally focused and the ‘British’ branded journals are less locally focused than the ‘American’ branded journals, and nation-branded sociology journals remain far more local in their focus than nation-branded political science journals.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".