Country names in journal titles: shaping researchers’ perception of journals quality
Bibliographic record
Abstract
Abstract Numerous academic journals incorporate geographic names, including countries and regions, in their titles. This practice is not uniform, as some journals opt to internationalise by omitting these affiliations. To gauge the impact of country names in journal titles on researchers' perceptions of journal quality, 408 researchers in sociology, psychology, environmental sciences, and physical chemistry in Brazil, Canada, Germany, Malaysia, Nigeria, and the USA were surveyed. The study reveals that most researchers believe that a journal's association with a specific country influences their perception of its quality (74.6%) and international readership (76.8%). Consequently, researchers tend to avoid journals with country-specific titles, suspecting limited readership or a predominant focus on papers from that country. However, exceptions exist, primarily in terms of perception, especially for American journals, which are often perceived as indistinguishable from mainstream international journals. Disciplinary variations emerge, with subject matter influencing perceptions. Subjects such as sociology, closely tied to local and national issues, exhibit a more (compared to e.g., chemistry) significant tendency toward recognising national journals. The inclusion of the term "international" in journal titles elicits mixed opinions, with some associating it with low quality or predatory journals, a perception that stems from the proliferation of predatory journals in some Asian and African countries. This study offers insight into researchers’ preferences and underscores the important role of journal titles in shaping researchers' perceptions of journals’ scope, quality and readership. In a challenging metric-driven research and publishing landscape, it is important to strike a balance between internationalisation and fostering diversity in scholarly journal publishing.
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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 | 0.036 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| 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".