Bibliographic record
Abstract
This chapter discusses the impact of television on spreading the popularity of the Olympics. The 2004 Summer Olympics in Athens attracted 3.9 billion unduplicated viewers with a cumulative total of 34.4 billion viewer hours. The 2008 Beijing Olympics attracted 4.3 billion viewers. The Winter Olympics also witnessed impressive viewership, with TV coverage of the 2010 Winter Games in Vancouver nearly twice as large as in Turin in 2006, and approximately three times the coverage in Salt Lake City in 2002. More fans around the world follow the Olympics than ever before because of broadcast coverage, providing an attractive audience for advertisers. The International Olympic Committee (IOC) knows this, and has been able to command higher broadcast revenues from higher fee premiums with each succeeding Olympics because of the significant, positive reputation and ongoing goodwill associated with the Olympic ideal. The broadcast revenue growth is impressive and coincides with the increasing sophistication of the Olympic Games as a viable marketing platform for companies. The revenue increases reflect not just the growing global stage that the Olympics command in a commercial sense, but also the credibility and reputation associated with the Games.
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 | 0.000 | 0.001 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.152 | 0.032 |
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".