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Record W2999676727 · doi:10.1002/ieam.4244

Factors Affecting the Perception of New Approach Methodologies (NAMs) in the Ecotoxicology Community

2020· article· en· W2999676727 on OpenAlexafffund
Matthieu Mondou, Gordon M. Hickey, H. M. Tuihedur Rahman, Steve Maguire, Guillaume Pain, Doug Crump, Markus Hecker, Niladri Basu

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Toxicology and Ecotoxicology
Canadian institutionsUniversity of SaskatchewanEnvironment and Climate Change CanadaMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaGenome PrairieGénome QuébecGenome Canada
KeywordsRespondentPerceptionPsychologyTest (biology)Logistic regressionEnvironmental healthApplied psychologyMedical educationPublic relationsMedicinePolitical scienceEcologyBiology

Abstract

fetched live from OpenAlex

Given current legislative mandates to assess the safety of thousands of chemicals and the slow pace at which conventional testing proceeds, there is a need to accelerate chemical risk assessment. Governments and businesses are increasingly interested in new approach methodologies (NAMs) that promise to reduce costs and delays. We explore 5 sociological factors within the ecotoxicology community that can influence the perception of NAMs: 1) professional profile (educational cohort, employer), 2) internal science communication within professional forums, 3) concern for "error cost," 4) collaboration across stakeholders, and 5) fundamental beliefs regarding toxicology. We conducted an online survey (n = 171; 2018) asking participants about their experiences and perspectives at events of the Society of Environmental Toxicology and Chemistry (SETAC) to assess 1) how NAMs are discussed compared to conventional testing and 2) how respondents perceive their viability. We developed ordered logistic regression (OLR) models to understand the influence of exploratory variables (cohort, core views on toxicology, frequency of collaboration) on respondents' evaluation of the viability of different NAMs. Our results showed that 1) NAMs were more likely than conventional methods to be challenged in forum discussions, which may be fueled by concerns for error costs in regulatory decision making; 2) perceptions of the viability of NAMs tended to follow a "pattern of familiarity," whereby respondents that were more knowledgeable about a test method tended to find it more viable; 3) respondents who agreed with the Paracelsus maxim had a greater likelihood of finding conventional testing viable; and 4) the more a respondent reported collaborating with industry on alternative testing strategies, the more likely she or he was to report that NAMs were less viable. These results suggest that there are professional and organizational barriers to greater acceptance of NAMs that can be addressed through a social learning process within the professional community. Integr Environ Assess Manag 2020;16:269-281. © 2020 SETAC.

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)0.020
metaresearch head score (Gemma)0.057
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.057
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.004
Scholarly communication0.0050.004
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.106
GPT teacher head0.324
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.

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

Citations30
Published2020
Admission routes2
Has abstractyes

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