Telecasting Tokyo to a Locked Down Nation: Australian Broadcast Coverage of the 2020 Olympic Summer Games
Bibliographic record
Abstract
This study explored how nationalism was perpetuated by the Seven Network’s broadcasting of the 2020 Tokyo Olympic Games during a time, in which much of Australia was in various forms of Covid-19 lockdowns. Self-categorization theory was used to analyze all the primetime coverage of the Seven Network’s main channel for name mentions, description of success or failure, and personality and physicality of the Olympians. Results of this study underscore large differences in the way in which the Seven Network portrayed Australian and non-Australian athletes. Whilst the majority of the top-20 most-mentioned athletes list were Australian, non-Australian athletes received the bulk of the name mentions. There were also differences in the ways in which Australian and non-Australian athletes’ success and failure were portrayed. This study contributes to the literature by uncovering how a major sporting event was covered by a national broadcaster during the Covid-19 pandemic and shows that Australian media catered its coverage to its home audience, who were in lockdowns. Thus, interest and viewership of the Tokyo Olympics was high, which might have been the impetus for the Seven Network to create a largely partisan program.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".