Chinese Journals' Chief Editors Should Enhance Their Response Rate to Authors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it