A Summer Country’s Coverage of a Winter Event: Australian Nationalistic Broadcast Focus of the 2018 Winter Olympic Games
Bibliographic record
Abstract
Broadcast commentary of sport programs often is seen as biased for the “home team.” This study sought to determine differences between how the media framed narratives of Australian and non-Australian Olympians by analyzing prime-time coverage of the 2018 PyeongChang Winter Olympic Games across all of Australia’s Seven Network channels. Because Australia is not a traditional powerhouse at the Winter Games, how the media portrays home team and foreign athletes is of interest in this summer sport country. Results revealed that overall, non-Australian athletes were covered and mentioned more frequently than Australian athletes. However, results found taxonomical differences in Seven Network’s depiction of Australian and non-Australian athletes’ successes—Australian success was attributed to athletic ability and courage, whereas non-Australians’ success was more frequently linked to intelligence, experience, and consonance. Differences in the attribution of failure by nationality were also found, with Australian’s failures more likely to be characterized by a lack of commitment and luck compared to their non-Australian counterparts. Athletes’ personalities also were described differently, with Australians receiving comments regarding their emotions, while non-Australians received either more neutral comments or had their appearance and body parts described more often. Theoretical and practical implications of this study are provided.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".