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Record W7135818101

The Comparison of News Values within text concerning handicapped and non-handicapped sport

2012· dissertation· cs· W7135818101 on OpenAlexaboutno aff
Anna Kulíšková

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2012
Typedissertation
Languagecs
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesBeijingPresentation (obstetrics)CzechRelation (database)Bachelor
DOInot available

Abstract

fetched live from OpenAlex

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...

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.003
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.313
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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