Bibliographic record
Abstract
This paper examines metaphors in live text football commentary, a developing genre of computer-mediated sports discourse. Drawing on Lakoff and Johnson’s CMT, live text commentaries of three final football matches were used as the data for the study. The commentaries included that of the 2025 UEFA Champions League, 2025 Conference League and 2025 Europa League. The commentaries were analysed using the Pragglejaz Group’s (2007) procedure for identifying metaphorically used words, enabling the study to systematically detect and classify lexical items whose contextual meanings diverged from their basic meanings. The results show that war, journey, construction and power domains were mapped onto various events in the football game. These domains functioned to frame football as a combative struggle (war), a contest for dominance (power), a structured and progressive movement toward a goal (journey), and a creative or strategic process (construction). The following conceptual metaphors were identified: A Football Match Is War, A Football Match Is Competition For Power, A Football Match Is A Journey and A Football Match Are Construction. The analysis provides support for the claim that metaphors are not merely stylistic choices but fundamental cognitive tools through which football discourse is structured and communicated.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".