The media image of the Clash of the Stars organization on selected news portals in first quarter of year 2022, 2023, and 2024
Bibliographic record
Abstract
This bachelor's thesis focuses on analysing the development of the media image of the combat sports organization Clash of the Stars during the observed period. The aim of the thesis is to compare changes in how this organization has been perceived in the media over time: from the first quarter of 2022, when the first event took place, through the first quarter of 2023-for more accurate interpretation of the results-and up to the first quarter of 2024, always with a one-year interval from the start of its activities. By comparing the collected data, it will be possible to determine how and in what direction the media image of the organization has evolved over time. The analysis is based on a quantitative content analysis of articles published on three major Czech online portals focused on combat sports. In its early days, the organization was mostly perceived in the media as a bizarre project built on controversial and scandalous personalities, although professional fighters also appeared in its events. Over time, however, Clash of the Stars gained wide public attention and established itself as one of the largest and most-watched platforms of its kind in the Czech Republic. Moreover, its activities have started to resonate even in international media. The thesis is divided into three parts. The theoretical...
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".