Pollution and Politicians: The Effect of PM on MPs
Bibliographic record
Abstract
Applying methods of textual and stylometric analysis to all 119,225 speeches made in the Canadian House of Commons between 2006 and 2011, we establish that air pollution reduces the speech quality of Canadian Members of Parliament (MPs). Exposure to fine particulate matter concentrations exceeding 15 μg/m3 causes a 3.1 percent reduction in the quality of MPs speech (equivalent to a 3.6 months of education). For more difficult communication tasks the decrement in quality is equivalent to the loss of 6.5 months of schooling. Our design accounts for the potential endogeneity of exposure and controls for many potential confounders including individual fixed effects. Politicians are professional communicators and as such the analysis contributes to our evolving understanding of how pollution exposure impacts the execution of work-relevant skills. Though we are cautious in interpreting the effect as a clean metric for performance, the effect size is around half that established in recent research for workers engaged in physical work tasks. Insofar as the changed speech patterns reflect diminished mental acuity the results make plausible detrimental effects of air pollution on productivity in a wider set of communication-intensive work settings.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".