Chinese Journals' Chief Editors Should Enhance Their Response Rate to Authors
Bibliographic record
Abstract
Chief editors are the souls of journals, and can guarantee a journal's success by enhancing the efficiency of the manuscript submission and publication process through promptness and speedy response rates to authors. In this study, a total of 867 international journals—indexed by Science Citation Index, Social Sciences Citation Index, and Arts & Humanities Citation Index, and 567 Chinese journals—indexed by Chinese Science Citation Database and Chinese Social Science Citation Information database, were randomly selected to explore whether significant differences in the response rate and speed exist between chief editors.639 chief editors' email addresses were obtained for the international journals, whereas 357 email addresses were gathered for the Chinese journals. However, due to mail servers, only 274 international and 330 Chinese editors were successfully contacted. All messages contained a questionnaire geared to determine the total length of time required for the manuscript submission and publication process. After two months, a 100% response rate was achieved for international chief editors, while Chinese chief editors had a significantly lower rate (P < 0.01) of 30.6%. Nevertheless, for both international and Chinese chief editors, 66% and 58% provided a response within 12 hours, respectively. Although several reasons exist for the Chinese journals' lagging behind international journals, this study demonstrates that the response rate of chief editors to authors may also be a contributing factor. Thus, chief editors of Chinese journals should enhance their response rate to improve the current situation and further contribute to Chinese journals' success.
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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 | 0.084 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".