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Record W3131152704 · doi:10.1111/risa.13701

Exploring Scientists’ Values by Analyzing How They Frame Nature and Uncertainty

2021· article· en· W3131152704 on OpenAlexafffundabout
Marco Vazquez, Bronwyn McIlroy‐Young, Daniel Steel, Amanda Giang, Gunilla Öberg

Bibliographic record

VenueRisk Analysis · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFrame (networking)Value (mathematics)Capital (architecture)Environmental ethicsEngineering ethicsEpistemologyPolitical scienceSociologyEngineeringComputer scienceGeography

Abstract

fetched live from OpenAlex

Several scholars have proposed that values embedded in science are a central reason why more research does not necessarily resolve scientific controversies around complex environmental issues. In the Capital Regional District, British Columbia, Canada, scientists have positioned themselves for and against the construction of a wastewater treatment plant in a debate framed as purely technical. This study explores the link between the scientists' positions in the debate and the way they, in their scientific publications, portray nature and environmental risks. We performed a qualitative content analysis of peer-reviewed publications by scientists who have publicly taken opposing positions in the controversy. We found that scientists against treatment predominantly frame nature as tolerant, up to a limit, to disturbances and potential risks, and they seem to embrace a view of science as capable of reducing uncertainties. In contrast, scientists in favor of treatment predominantly portray nature as fragile, particularly toward human-mobilized environmental risks and they commonly present scientific uncertainty as worrisome based on potentially harmful consequences. Our study suggests that value-laden perspectives impact scientists' positions even in a seemingly technical controversy.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.309
GPT teacher head0.414
Teacher spread0.104 · 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 teacher head, 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

Citations4
Published2021
Admission routes3
Has abstractyes

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