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Record W2777630948 · doi:10.5539/ijel.v8n2p244

Pragmatics of Sports News Reports

2017· article· en· W2777630948 on OpenAlexvenueno aff
Fareed Hameed Al-Hindawi, Ramia Fu’ad Mirza

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldComputer Science
TopicVideo Analysis and Summarization
Canadian institutionsnot available
Fundersnot available
KeywordsPragmaticsRelevance (law)Computer scienceDialecticFootballBridging (networking)Logical analysisStatistical analysisPsychologyLinguisticsPolitical scienceMathematical statisticsEpistemologyMathematicsStatisticsComputer security

Abstract

fetched live from OpenAlex

The paper aims to build the structure of sports news reports by bridging them to pragmatics hence coming up with their pragmatic structure. Accordingly, two pragmatic concepts (viz. cooperative principle and speech acts) are believed to be helpful in performing this task. Moreover, the notion of dialectical relevance, developed by Walton (1995), is utilized. This is done by pragmatically analyzing eight news reports of different sports (football, tennis, formula1, and golf, respectively) taken from the BBC sports official website. The analysis is carried out by means of an eclectic model developed by the study itself to serve this aim. This type of analysis is supported by a statistical analysis to quantitatively validate its findings. The two types of analysis basically show that there are no major differences between the games analyzed as to employing the various strategies compiling the aforementioned analytic tool.

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)0.091
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.923
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.091
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.284
Teacher spread0.271 · 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 designNot applicable
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
Published2017
Admission routes1
Has abstractyes

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