MétaCan
Menu
Back to cohort
Record W2908372700 · doi:10.1558/japl.32093

The death of scientific evidence in Canadian policymaking:

2016· article· en· W2908372700 on OpenAlexaffabout
Graham Smart

Bibliographic record

VenueJournal of Applied Linguistics and Professional Practice · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsArgumentativeArgument (complex analysis)IdeologyRepresentation (politics)Government (linguistics)Public relationsPolitical scienceSociologyOrder (exchange)EpistemologyPositive economicsLawPoliticsLinguisticsBusinessEconomics

Abstract

fetched live from OpenAlex

This study examines publicly voiced resistance by a Canada-wide community of scientists and citizen supporters against what they perceived as the Canadian government's efforts to undermine publicly supported science, with its concern for empirical evidence, in order to facilitate a narrowly pro-industry orientation in its policy-making. Using Hajer's argumentative discourse analysis (ADA) to interpret a corpus of some 700 Web-published texts, the author identified a macro-argument collectively produced and publicly communicated by the Canadian scientific community. The study also showed how this macro-argument served as a vehicle for two ideological representations: a virtuous self-representation of the scientific community itself and a negative representation of the motives and actions of the Canadian government. The findings of the research contribute to our understanding of how collective argumentative positions emerge within the discourse of a major scientific controversy. At the same time, the study offers policymakers insights in how they might communicate more effectively with communities of scientific experts.

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)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.062
GPT teacher head0.367
Teacher spread0.305 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied Linguistics and Professional PracticeSame topicDiscourse Analysis in Language StudiesFrench-language works237,207