Analysis of the media coverage characteristics on radiation safety issues of the Saint-Petersburg and the Leningrad region population
Bibliographic record
Abstract
The purpose of the study was to examine the quantity and quality of publications on radiation safety of the population in the media on the example of Saint Petersburg and the Leningrad region for the period of the first three-quarters of 2016. An analysis of publications in the media is an essential part of the work on the formation of an adequate perception of the radiation risk by the population. The Information and Analytical Centre of Rospotrebnadzor on radiation safety of the population developed a pilot computer-assisted system for the media publication analysis. The study was performed by this system as a development and improvement of the Center’s works applicable to the registration, storage, and analysis of qualitative and quantitative information contained in the publications. The author selected 27 mass-media sources for analysis: 8 newspapers (2 of them are located in Sosnoviy Bor); 8 TV-channels (4 – federal, 2 – regional, 2 – local in Sosnoviy Bor); 10 online media and the web-site of the Sosnoviy Bor administration. During the analyzed period, 1075 informational materials on issues of radiation safety were collected and added to the database. The largest number of publications were in the second quarter of 2016. The peak of publication activity on issues of radiation safety was registered in April. This fact is related to the 30th anniversary of the Chernobyl accident. A significant part (over 50%) of the publications were neutral in all media and in different types of media. A significant part of the publications is a brief informational note with the neutral nature of the character of the information. The number of materials with negative character of information among the publications on the subject of “radioactive waste” is more than 2 times larger than that for the publications on the subject of “nuclear energy”. The majority of publications belongs to the information genre. Analytical materials are a minor part and range from 1.6% to 8 % depending on the type of media. The testing of the computer-assisted system on the media publications analysis has shown its efficiency in terms of speed and agility of analysis and remote maintenance of the database, the possibility of further use it to analyze publications in media at the organization and optimization of risk communication in regions of the Russian Federation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".