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Record W2808002441 · doi:10.1177/0162243918781269

What Scientists Say about the Changing Risk Calculation in the Marine Environment under the Harper Government of Canada (2006-2015)

2018· article· en· W2808002441 on OpenAlexafffundabout
Allain Barnett, Melanie G. Wiber

Bibliographic record

VenueScience Technology & Human Values · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsUniversity of New Brunswick
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGovernment (linguistics)ReflexivityRisk societyDemocracyPolitical scienceSociology of scientific knowledgePrivate sectorScience policyPublic relationsEnvironmental ethicsSociologyPublic administrationSocial scienceLawPolitics

Abstract

fetched live from OpenAlex

This paper examines how the Harper Government of Canada (2006-2015) shut down both debate about threats and research into environmental risk, a strategy that Canadian scientists characterized as the “death of evidence.” Based on interviews with scientists who research risks to the marine environment, we explore the shifting relationship between science and the Canadian government by tracing the change in the mode of risk calculation supported by the Harper administration and the impact of this change. Five themes emerged from the interviews: erosion of science research capacity, resulting limitations in understanding risk, declining influence on policy and regulation, redirection of public science funds to support the private sector, and the need to broaden the science knowledge base. The Canadian death of evidence controversy represents a challenge to science and technology studies (STS) scholars who wish to maintain a critical and reflexive perspective on the scientific enterprise without supporting attacks on evidence. While subsequent Canadian governments may simply return science to an unreflexively privileged knowledge status, we view this as equally damaging to broad risk calculation and democratic science. We suggest instead that a broader gathering of matters of concern will always be essential to risk assessment.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativelow
gptScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.027
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.873

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.049
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0320.031
Scholarly communication0.0180.005
Open science0.0020.005
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.298
Teacher spread0.283 · 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

Labeled directly by 2 models reading the full record.

Study designQualitative
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

Citations11
Published2018
Admission routes3
Has abstractyes

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