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Record W4401441482 · doi:10.1093/polsoc/puae026

Words not deeds: the weak culture of evidence in the Canadian policy style

2024· article· en· W4401441482 on OpenAlexaffabout
Andrea Migone, Michael Howlett, Alexander Howlett

Bibliographic record

VenuePolicy and Society · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsUniversity Canada WestSimon Fraser University
Fundersnot available
KeywordsStyle (visual arts)Political sciencePolitical economySociologyPositive economicsLaw and economicsEconomicsHistory

Abstract

fetched live from OpenAlex

Abstract The Canadian policy style has been described as one of overpromising and underdelivering, where heightened expectations are often met by underwhelming outcomes. Here, we examine the evidentiary style of Canadian policy-making which undergirds and reflects this policy style, particularly the nature of the policy advisory system that contributes to this pattern of policy-making. We do so by assessing how the different components of the advice system, which include academics, consultants, and policy professionals within the public service, are structured and relate to each other within the overall dynamics of information management and policy formulation in the governments of Canada. Using examples from recent efforts to revitalize Canadian government, the paper argues that the federal government in particular shows a pattern of the predominance of non-innovative academic “super-users,” distributed policy shops, and process-oriented analysts and consultants who combine with attributes of federalism and partisan budgetary politics to drive a distinctively fragmented and procedurally-oriented federal policy-making process. In these processes, evidence is often secondary to political posturing and short-term electioneering in program creation and execution, contributing greatly to the national policy style set out above.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.100
GPT teacher head0.398
Teacher spread0.297 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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