Bibliographic record
Abstract
Air pollution is a major environmental problem that poses numerous health risks to those exposed to it. The adverse health effects are compounded in a place as dense as Hong Kong and further intensified due to its proximity to industrial and manufacturing plants across the border in Mainland China. Hong Kong has attempted to address the issue of air pollution through the enactment of legislation and policies such as the 1983 Air Pollution Control Ordinance and Air Quality Objectives, but so far these measures have not proven to be particularly effective. The new Air Quality Objectives are not as stringent as claimed by the Environmental Protection Department, the legal system is limited in enforcing air pollution legislation, and the Air Pollution Control Ordinance itself is flawed at best and requires urgent attention. \n \nAn examination of the air pollution legislation and policies of overseas jurisdictions such as Ontario, California, and Tokyo show that these regions have made significant progress in addressing air pollution by prioritizing public health and utilizing a variety of measures to reduce air pollution. Ontario’s Environmental Protection Act gives the Ministry of the Environment numerous ways to deal with polluters and the Environmental Bill of Rights helps facilitate public participation in environmental decision-making by granting the public the right to apply for an investigation or review of existing legislation and policies. California’s Air Resources Board has been commended for enacting air pollution regulations that are more stringent than national standards and many states have adopted the Golden State’s standards as their own. In Japan, government, corporate, and public responsibilities for reducing air pollution and protecting the environmental are outlined in the Basic Environmental Law. Legislation providing compensation to victims of pollution as well as the use of a Total Mass Emissions Control system has helped the country achieve an extraordinary rate of compliance with national air quality standards. \n \nBy studying, modifying and applying air pollution control legislation and policies being used in the three jurisdictions to its own Air Pollution Control Ordinance and air pollution control management as a whole, Hong Kong can be better prepared to protect public health and it’s environment in the future.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".