The Pragmatics of Amusement in Selected British Football Commentaries
Bibliographic record
Abstract
Sport in general and football in particular have become the most popular form of amusement nowadays throughout the special performance of the commentators who comment on the game. Their duty is to provide the audience with the relevant information about what is happening during the game and to amuse them at the same time. They often do that by using specific linguistic features. The present study mainly tackles amusement in British football commentary language from a pragmatic point of view by selecting (2) football matches of (4) British commentators who are Martin Tyler, Andy Gray, Alan Perry and Gary Neville. As such, it is carried out with the aim of exploring the phases of commentaries according to which the football commentary is considered amusing, identifying the strategies of commentary used by the commentators in each phase, specifying the pragmatic devices used in each strategy of football commentaries which make these commentaries amusing. On the basis of the analysis, the following conclusions can be made: The commentaries are achieved in three phases, i.e., play-by-play, colour commentary and action replay which make the commentaries as amused, the commentaries are structured out of three strategies, descriptive, dramatic and humorous, and in the whole pragmatic structure of amusement in football commentaries, each strategy is variously fulfilled by means of certain pragmatic devices associated with it to achieve amusement.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".