A Survey of Enhanced Publication Features of China’s Science and Technology Research Journals in 2018
Bibliographic record
Abstract
Enhanced publication features that extend information access, add variety to presentation formats, and improve reader comprehension have become a part of China’s academic journals in science and technology (sci-tech for short) in recent years. We sought to determine the degree of their adoption. By surveying 472 Chinese sci-tech journals, we found that 102 of these journals had enhanced publication features. Thus less than a quarter of Chinese sci-tech journals in our sample had adopted enhanced publication at the time of our survey. Moreover, the enhancing features of the 102 journals were mostly simple ones, which did not depend on authors providing supplemental content. More of these 102 journals are published by scholarly associations than by other types of publishers, and the disciplinary distribution of the journals was imbalanced, with the discipline of medicine and health having the lowest percentage of journals with enhanced features among those disciplines with such journals. This finding is out of step with large international publishers, whose medical journals frequently have features of enhanced publication. These results reveal a gap between the practices of large international publishers and those of China’s publishers when it comes to adopting enhanced publishing features for sci-tech 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".