Environmental and Public Health Risks from Air Pollution at the Beijing 2008 Olympics
Bibliographic record
Abstract
China has made enormous strides in tackling its environmental problems, but considerable work remains to be done. In an article on environment and public health published in the Winter 2007 edition of this journal, the question was raised whether the forces unleashed by China’s aggressive approach to economic growth since the late 1970’s --- two to three times the global average --- were too strong to be controlled by its environmental policies. The same question remains relative to health risks for the Summer Olympics this August (2008): “The main problem appears to be that well intentioned public health and environmental policies have not yet been realistically integrated into overall policies which emphatically promote economic growth.”1 In other words, theory and practice are in conflict and in practice, China has been promoting objectives that are diametrically opposed. In August, 2007, China conducted a dry run of procedures to control air pollution by restricting car use. The results were hard to interpret: official websites claimed success, while other observers and official data showed varied results of successful pollution reduction (see below). Overwhelmingly the major health as well as environmental concern for Beijing is air pollution and solutions have concentrated on the city itself, but surrounding areas are also problematic and have not been addressed as well. This article will look at the health risks to athletes and the preparations that the Chinese government has been making to forestall widespread air pollution for the games. One of the main reasons Beijing was chosen for the 2008 Olympics over Toronto and Paris was its proposals to have a “green Olympics.”
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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.002 | 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".