Exporting China’s Scholarly Books: Current Conditions for Chinese Publishers
Bibliographic record
Abstract
The purpose of this article is twofold: a) to analyse the current situation of Chinese academic book exports from a publisher’s perspective, including models, channels, motivation, and performance; b) to identify the challenges that Chinese scholarly publishers are facing and explain their causes. In all, fifteen publishers from state presses, local presses, and university presses were interviewed. Desk research supplemented the interview data. We found that co-publishing, copyright transfer, and physical book export are the main models and that publishers prefer co-publishing and copyright transfer to exporting actual books. Book fairs and copyright agents are still important channels for negotiating export deals. Applying for funding programs and achieving evaluative benchmarks are the principal motivators for publishers. Surprisingly, over half the publishers interviewed do not profit much from exportation. Nevertheless, supervising departments and chief managers still attach much importance to it. At present, Chinese scholarly publishers are confronted with the challenges of a quality gap, state subsidy substituting for a real market, and information asymmetry. Unreasonable systems of academic evaluation and quality control, state-owned property rights, limited qualified manpower, and rare cooperation are key causes of these challenges.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".