Information Presentation of Professional Structure of Russian Society in Mass Media
Bibliographic record
Abstract
The paper analyzes the processes of mass media data content impact on social processes taking place within society. The key role of modern mass media in person’s life as a society’s information base is brought into focus. In this context the problem of circulating information quality and adequacy of information presentation of social processes of mass media is stated. The results of mass media activity influence many social processes, particularly the process of society’s professional structure formation. When commenting profession representatives and professional activity it forms certain image of society’s professional structure. Mass media influence on real occupational skill structure of society makes itself felt through the formation of social & professional hierarchy in person’s consciousness, the hierarchy closely related to the idea of status value of one or another profession, influencing on occupational choice. The results of the empiric study of information presentation of professional structure of the Russian society in mass media realized by means of content analysis of print media publications are represented. Regression model is built based on collected data to study interrelation between a number of factors such as actual professional structure of the Russian society, information presentation of professional structure created by mass media, need for specialists, average salary, status value of professions, and various professional groups.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".