Bibliographic record
Abstract
In the context of globalization and digital transformation, the regulation of the publishing industry is becoming increasingly relevant, as only balanced state, institutional, and professional approaches can ensure the sustainable development of the book sector. The Ukrainian model of publishing regulation is still in the process of formation, and studying international experiences is a necessary step toward adapting effective practices to the national context. This article aims to analyze publishing regulation models in countries such as Germany, France, the United Kingdom, Canada, Poland, and Australia, with a focus on institutional mechanisms, legal frameworks, forms of financial support, and the role of professional associations. It demonstrates that several typical models exist-interventionist, corporatist, liberal-regulated, federal, and transitional-which differ in the degree of state involvement, the level of autonomy of the professional community, and the motivation systems for preserving cultural diversity in publishing. The purpose of the research is to systematize global regulatory models of the publishing industry and identify elements that may be relevant for implementation in Ukraine. The article examines contemporary theoretical and methodological approaches to regulation in the creative industries, with a particular emphasis on publishing. It outlines key models of state intervention and self-regulation based on the examples of France, Germany, the UK, Canada, Poland, and Australia. Legal, economic, and institutional tools used to support the publishing sector are analyzed, and practices relevant to the Ukrainian context are highlighted to strengthen cultural policy effectiveness in the book sector. The methodological framework is based on comparative analysis using general scientific methods of systematization, generalization, and content analysis of legal acts, reports, and analytical materials from professional organizations. The scientific novelty lies in the attempt to typologize publishing regulation models in the global context and outline ways to integrate best practices into the Ukrainian book market system. The study concludes that successful models are characterized not only by stable legal frameworks but also by ongoing cross-sectoral co- operation-between the state, professional associations, educational institutions, and international donors. Adapting elements of these models to Ukraine may enhance the efficiency of the publishing industry, ensure its sustainable development, and strengthen its cultural mission.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".