Bibliographic record
Abstract
Poor people may be more susceptibie, but poverty also fosters increased pollution This issue of the journal includes three papers that touch on relations among socioeconomic status (SES), health, and air quality. Jerrett et al considered whether SES differentials in Hamilton, Ontario, modify the temporal relations between daily mortality and either coefficient of haze (COH) or SO2.1 Martins et al did a similar analysis with respect to PM10 in Sao Paulo, Brazil.2 The third paper, by Gouveia et al , also involved Sao Paulo but examined cross sectional relations between several pollutants and infant birth weight.3 As such, it involves SES factors only implicitly, by virtue of the trends seen by Martins et al 2 that link levels of PM10 in Sao Paulo with residence in slums and other SES indicators. Inequitable distribution of environmental impacts within a city or region may raise issues of “environmental justice”,* but it may also be possible to get additional insights into the implied health relations by probing a little deeper into the nature and origins of such differential impacts. Most time series studies are based on entire cities and spatially averaged air quality, in order to maximise statistical power and to preclude the necessity of assigning individual deaths to specific air quality monitors. Time series studies avoid SES confounding by design, as those factors do not vary on a daily basis. However, many air pollutants tend to vary in concert, especially those that are co-emitted by common sources, thus making it difficult to identify the most probable causal agent. Cross sectional studies may have less co-pollutant collinearity, but can suffer from SES confounding to the extent that SES may tend to decrease with residential proximity to major pollution sources. All of these issues are in …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".