The Comparison of News Values within text concerning handicapped and non-handicapped sport
Bibliographic record
Abstract
Presented in this bachelor thesis is a qualitative analysis that is focused on the comparison of news values, in relation to athletes with a disability and athletes without a disability, in articles published in Mlada Fronta DNES (a Czech newspaper) at the time of the 2008 Summer Olympic and Paralympic Games in Beijing. In addition, the further analysis will be done, of the news values that are given to Olympians and the values given to Paralympians, and whether these preferences are reflected in the overall presentation of both groups of athletes. It can be concluded that the articles about the Paralympics are shorter, more vague and overall of a much lower standard than their Olympic counterparts. Indeed the reader does not have to get full picture of how the games went, who won, who lost, and why and how the athletes lived in Beijing. The Olympic and Paralympic Games in Beijing are the most recent top athletic events that can be compared in terms of news coverage. It is on purpose that the Winter Olympic and Paralympic Games in Vancouver (2010) are overlooked, as even though they are more current, there are major differences between the numbers of athletes and sports, which, in terms of news coverage, make them incomparable. The thesis further examines and compares the information about Olympic...
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".