Realization of Copyright in Russia in the Sphere of Scientific Articles: The Experience of Applied Sociological Analysis
Bibliographic record
Abstract
In this paper we on the basis of expert interview method generalize and analyze real social practices of Russian scientific journals linked to the realization of copyright allocation when publishing scientific articles. It was found that conclusion of written copyright agreement is performed only in exceptional cases and there are three types of such cases. The analysis of copyright allocation between an author and a journal showed the dependence of the allocation on the journal’s policy. As a rule, practically all the property rights, except for the right for author’s copy, are passed to journal. We also analyze the conditions for allocation of journal’s issues in the Internet. We have established that policy of Russian journals often includes granting of open access to articles. Main types of violations committed by Russian authors in respect of journals are generalized. The authors of the paper come to the conclusion that despite the fact the legislative framework concerning turnover and protection of intellectual property in the Russian Federation is by and large formed, the everyday practice in this area demonstrates significant stagnation in implementation of legally set standards. The realization of copyright in the sphere of publication of scientific articles in Russian journals in everyday practice is mainly regulated by the system of informal standards. Among them there are still the standards applied in the Soviet times.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".