National lenses on a global news event: determinants of the politicization and domestication of the prelude to the Beijing Olympics
Bibliographic record
Abstract
This article utilizes a 14-country comparative data set and analyzes how different countries' television news has covered the events and controversies surrounding the Beijing Olympics in the months before the Summer Games. Conceptually, the focus is on the notions of domestication and politicization. However, rather than simply illustrating the presence of these phenomena, the analytical interest resides mainly in uncovering their “determinants”. More specifically, following the arguments that the mainstream news media are generally power dependent on the one hand and have a strong local orientation on the other, it was hypothesized that a number of relationships exist between degrees of domestication and politicization in television news in different countries and the countries' social, cultural, and political characteristics. The empirical results show that the degree of domestication can be explained by a country's level of participation in the Olympic Games and the size of the country's ethnic Chinese population. The degree of politicization, meanwhile, can be explained by the type of political regime and the country's economic relationship with China. The implications of the findings are discussed.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".