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Record W4379375122 · doi:10.3386/w31313

Laboratory Safety and Research Productivity

2023· report· en· W4379375122 on OpenAlexfundno aff
Alberto Galasso, Hong Luo, B B Zhu

Bibliographic record

VenueNational Bureau of Economic Research · 2023
Typereport
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of California, Davis
KeywordsProductivityData scienceEngineeringComputer scienceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Are laboratory safety practices a tax on scientific productivity?We examine this question by exploiting the substantial increase in safety regulations at the University of California following the shocking death of a research assistant in 2008.Difference-in-differences analyses show that relative to "dry labs" that use theoretical and computational methods, the publication rates of "wet labs" that conduct experiments using chemical and biological substances did not change significantly after the shock.At the same time, we find that wet labs that used dangerous compounds more frequently before the shock reduced their reliance on flammable materials and unfamiliar hazardous compounds afterward, even though their overall research agenda does not appear to be affected.Our findings suggest that laboratory safety may shape the production of science, but they do not support the claim that safety practices impose a significant tax on research productivity.

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)0.014
metaresearch head score (Gemma)0.088
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.088
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.004
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.368
GPT teacher head0.519
Teacher spread0.151 · 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.

Study designObservational
DomainMethods
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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