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Record W7135469237

Comparative analysis of News Broadcasting on Czech Television and TV Nova during the fire in Bohemian Switzerland

2023· dissertation· cs· W7135469237 on OpenAlexaboutno aff
Zuzana Dumbrovská

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2023
Typedissertation
Languagecs
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsCzechBroadcasting (networking)NoonPeriod (music)Nova (rocket)NewspaperNova scotiaOrder (exchange)
DOInot available

Abstract

fetched live from OpenAlex

The thesis focuses on the comparison of noon and evening news spots in Czech Television and TV Nova. The aim is to find out how much broadcasting time is devoted to the fire in Bohemian Switzerland, which is considered to be one of the largest and worst fires in the Czech Republic. We examine the period from the outbreak of the fire for approximately a week, when the situation is still on going (more precisely 24.7. - 31.7.2022). We analyze the following sessions: Události and Televizní noviny, Zprávy ve 12 and Polední Televizní noviny. We will use a quantitative analysis to provide clear, accurate and verifiable results. In addition to numerical analysis, we will also focus on the order of individual news items in the broadcast and compare events in Bohemian Switzerland with other events that have occurred in Czech Republic and around the world. The aim is to determine whether the media is subject to campaignibility, which means that media favors a particular issue over others, regardless of their actual importance. The results of this thesis could be used for further analyses in the future, which could include a larger research period and more media types.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.314
Teacher spread0.290 · 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; a candidate call from one teacher head, not a consensus.

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
Published2023
Admission routes1
Has abstractyes

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