Bibliographic record
Abstract
Purpose The purpose of this paper is to review the management of air pollution in Mexico and strategies that have been considered to correct the issues, including potential future directions to further improve air quality for Mexico’s environment and people. Design/methodology/approach Different serious academic databases were searched for material regarding the issue of air pollution in Mexico, such as Scopus and Social Science Citation Index. Regional concern was an important factor that was considered in this review. Material was considered based on its recency, academic importance and veracity. The studies selected mainly ranged from the mid-1990s to 2018. Findings Air pollution in Mexico has been a primary issue for the country’s administration and that of Mexico’s North American neighbour, the USA. It has contributed significantly to climate change and has had detrimental effects on both the environment and on the health of Mexican citizens in various ways. While efforts to ameliorate the situation have been relatively strong, it is hoped that ongoing cooperation between Mexico, the USA and Canada will influence the development of stricter emissions standards. Originality/value This paper considers current circumstances and whether enough has been done to mitigate Mexico’s significant air pollution problem. It also considers several recommendations made by commentators as to potential future directions to rectify the issues, as no similar review has been made for a developing Country.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".