MétaCan
Menu
Back to cohort
Record W4323825345 · doi:10.1086/724951

Clean Air and Cognitive Productivity: Effect and Adaptation

2023· article· en· W4323825345 on OpenAlexafffund
Nikolai Cook, Anthony Heyes, Nicholas Rivers

Bibliographic record

VenueJournal of the Association of Environmental and Resource Economists · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsResource (disambiguation)ProductivityAssociation (psychology)Resource useDownloadResource productivityCognitionAdaptation (eye)Political sciencePsychologyLawNatural resource economicsNatural resourceEconomicsEconomic growthPsychiatryComputer science

Abstract

fetched live from OpenAlex

We observe 1.8 million university course grades for 88,959 adults who learn and complete examinations in a much less polluted environment than previously studied. We use a within-student identification strategy and find robust evidence of a negative and causal effect of exam-day outdoor air pollution on course performance. The effect of pollution persists beyond the same-day effect. Female students are more sensitive than males, and effects are greatest when students are engaged in unfamiliar tasks. We explore two margins of adaptation, one infrastructural, one behavioral. Working in a new building, and particularly if it is high quality (LEED Gold), provides significant mitigation. Relocating to a floor above ground level also offers partial protection.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.012
GPT teacher head0.230
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the Association of Environmental and Resource EconomistsSame topicAir Quality and Health ImpactsFrench-language works237,207