Process efficiency and outcome effectiveness in the united kingdom’s local air quality management regime
Bibliographic record
Abstract
The UK's Local Air Quality Management (LAQM) commences with a Review and Assessment which may lead to the declaration of an Air Quality Management Area (AQMA) where an exceedence of the Air Quality Objectives is confi rmed.A declaration initiates the development of Air Quality Action Plan (AQAP) intended to provide solutions to the identifi ed problems.There is no doubt that the LAQM framework has delivered a clear picture of elevated air pollution at specifi c locations in the UK, defi ned in temporal and spatial scales.However, the evidence to date suggests that delivering solutions to air quality problems is much more problematic, and has not been achieved at the rate expected when the framework was introduced in 1997.Despite the national policy intention and direction provided through the framework, the probability of achieving the traffi c-related Air Quality Objectives by the set dates in the UK Regulations is uncertain.Using evidence from several studies undertaken by the authors, this paper considers the implication of distinct policy disconnects which are present in the LAQM process.The key conclusion implies transition from procedural compliance with the diagnostic process of LAQM towards a more holistic approach that will require new means of internal communication and co-operation and external consultation at the local and central government level and the ability to confront political and economic vested interest.
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 | 0.048 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".