Covering the Home Nation at Its Home Games: An Analysis of Australian Nationalistic Broadcast Coverage of the 2018 Commonwealth Games
Bibliographic record
Abstract
This study explored how nationalism unfolded within the Australian broadcast of the 2018 Commonwealth Games that were held on the Gold Coast, Australia. Applying social-categorization theory, over 31 hours of the total coverage was content analyzed for name mentions, description of success or failure, and personality and physicality of the athletes. Results of this study underscore large differences in the amount of commentary that was provided to Australians and non-Australians during the broadcasts, with Australians being mentioned more than non-Australian athletes. As Australia performed well at the Commonwealth Games, Australians featured highly on both the top most-mentioned athletes list and the overall percentage of name mentions also favored Australians. The Seven Network emphasized Australian athletes to its viewers, as Australian viewers would share many of the group characteristics with athletes who were featured on television. This study contributes to the literature by uncovering how in-group members were portrayed in the Australian sports context while also providing insight into how consumers’ media consumption could potentially affect how the network broadcasts the Commonwealth Games from a nationally partisan perspective.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".