The Internationalization of Chinese English-Language Humanities and Social Science Journals
Bibliographic record
Abstract
This paper surveys the status of Chinese English-language journals in the humanities and social sciences (HSS-CELJs). HSS-CELJs are an important vehicle for disseminating Chinese scholarly voices and culture throughout the world. We used a mixed-methods approach to investigate the status of HSS-CELJs according to a number of attributes: growth rate over time, type of publisher, discipline, region of publication, publishing frequency, independence versus co-publication, and inclusion in citation indexes. We discuss some of the challenges facing HSS-CELJ publishing and highlight several contradictions of internationalization in the Chinese context. As of March 2020, eighty-seven HSS-CELJs covered nineteen disciplines, among which economics (17 per cent) and law (13 per cent) accounted for the highest proportions. The establishment of HSS-CELJs has increased significantly since 2004. Fifty-two per cent of HSS-CELJs were jointly operated with international publishers under two different models of cooperation, and twenty-eight (32 per cent) were indexed in international databases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".