Bibliographic record
Abstract
The Railway in the News Media takes the railway topic as a specialized one of those the media deal with as a part of its general news.. The rail-news usually does not possess its own section - in contrast with some other specialized news topics, the railway news usually does not possess its own section. The railway topics occur in the economic, domestic or even the regional sections. Nevertheless, there are people specializing themselves in this topic in editorial staffs. Therefore, it is questionable whether the reservations about the quality of the rail-news are justified. This work attempts to research these processes. It deals with the professionalism of the rail-news processing. The work monitors impartiality of the news on the one hand and using of language and semiotics on the other hand. The basis of the work is a quantitative analysis of media contents dedicated to railway in selected printed media. The output is a summary of accuracy of reporting on operational, technical and historical facts about railway and of terminological correctness. It also examines the balance of railway subjects coverage in media. The research results in Czech news media making mistakes in specialized topic news. However. the mistakes are not too serious. Approximately one quarter of analyzed news contents were...
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".