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Record W7135627972

Comparison of broadcasting hockey tournament at the Olympic Games in 1998 and 2010, focusing on commentary

2011· dissertation· cs· W7135627972 on OpenAlexaboutno aff
Michael Bereň

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2011
Typedissertation
Languagecs
FieldSocial Sciences
TopicEducation, Psychology, and Social Research
Canadian institutionsnot available
Fundersnot available
KeywordsTournamentBroadcasting (networking)InterviewBachelorWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

Bibliografický záznam BEREŇ, Michael. Srovnání vysílání hokejového turnaje na olympijských hrách v roce 1998 a 2010 se zaměřením na komentář. Praha, 2011. 152 s. Bakalářská práce (Bc.) Univerzita Karlova, Fakulta sociálních věd, Institut komunikačních studií a žurnalistiky. Katedra žurnalistiky. Vedoucí diplomové práce Prof. PhDr. Jiří Kraus, DrSc. Abstract The goal of the bachelor thesis is comparison of broadcasting of hockey tournaments at the Olympic Games in 1998 (Japan, Nagano) and 2010(Canada, Vancouver). My work has to demonstrate, how the sport live broadcasting has changed in 12 years, especially thanks to HD broadcasting technology. In this work I will focus especially on commentary. I will compare the role of color commentator ("analyser") and profi-commentator ("play-by-play") . I will describe the commentator's work along with historical background in theoretical part. In practical part I will try to examine audience reactions according to my own case study and complete them by case studies and surveys that have already been published. Interviewing of commentators will be the main source for this work.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.011
Science and technology studies0.0040.002
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.047
GPT teacher head0.387
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2011
Admission routes1
Has abstractyes

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