Bibliographic record
Abstract
Emerging infectious diseases (EIDs) pose international security threats because of their potential to inflict harm upon humans, crops, livestock, health infrastructure, and economies. Despite the scale of this threat, there are inherent limitations in preventing and controlling EIDs, including the scope of current disease surveillance efforts. All of this leads to the following questions in the context of Mexico's recent swine flu experience: What were the cultural, political, and economic challenges to Influenza A/H1N1 virus response in Mexico? By way of comparison, what can we learn from the U.S. experience in 1976 with A/New Jersey/76 (Hsw1N1), later referred to as H1N1? This article explores the comparative political economy of Mexico's handling of influenza virus A/H1N1 outbreak in 2009. Research provides notable observations-based on the strengths and weaknesses of each country's response--that can be used as a starting point of discussion for the design of effective EIDs surveillance programs in developing and middle-income countries. In the U.S., the speed and efficiency of the 1976 U.S. mobilization against H1N1 was laudable. Although the U.S. response to the outbreak is seldom praised, the unity of the scientific and political communities demonstrated the national ability to respond to the situation. Mexico's strongest characteristics were its transparency, as well as the cooperation the country exhibited with other nations, particularly the U.S. and Canada. While Mexico showed savvy in its effective management of public and media relations, as the article details, political, economic, and cultural problems persisted.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".