Проблематика темы «Арктика» в российских и зарубежных СМИ: управленческий аспект
Bibliographic record
Abstract
Topicality of the subject. Solution of long-range tasks that have crucial geopolitical importance for subarctic states due to the development of the Arctic region territories. This trend draws mass media attention to the problems connected with that region. Purpose. Research of the main trends of the Arctic region subject in the Russian and foreign mass media. Methods. The author monitored mass media including the Russian language newspapers, information agencies, Internet-editions and foreign media-resources of the USA, Canada, Norway and Denmark for the period from January 1, 2014 up to September 1, 2014. The author emphasized five topical segments and researched references to the topics. Results. Monitoring allowed determining amount of attention paid by editions to each segment of the Arctic region subject. Comparison of articles in national and foreign mass media gave a chance to consider the same subjects differently, to see different aspects of the issues, which are topical for the region. The author identified information trends connected with the development of the Arctic region and determine the most noticeable information grounds for the researched period. Based on the analysis of the researched data the author pointed out a drawback in description of a social aspect of the region development. Scientific novelty. Scientific novelty is in analysis of volumes of the Arctic region subjects in mass media, finding out gaps in covering certain topical segments.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".