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Record W2574129544 · doi:10.1017/s0008423916000974

Richard Simeon and the Policy Sciences Project

2016· article· en· W2574129544 on OpenAlexaffabout
Michael Atkinson

Bibliographic record

VenueCanadian Journal of Political Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Science and Policy Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPolicy SciencesNormativePrincipal (computer security)Public policyPolitical sciencePublic administrationState (computer science)Policy analysisSet (abstract data type)Policy studiesScience policyLaw and economicsSociologyLawComputer science

Abstract

fetched live from OpenAlex

Abstract In his classic 1976 article on the state of policy studies in Canada, Richard Simeon explicitly warned against following the path toward a policy science. Simeon was suspicious of the normative agenda embedded in the policy sciences project and worried that it would submerge politics in a broader set of interdisciplinary concerns. Was Simeon right? The policy sciences have not developed the way their principal proponent, Harold Lasswell, had anticipated or hoped, but neither has the study of public policy developed exactly as Simeon advocated. Both Lasswell and Simeon believed strongly in an empirical orientation and Lasswell, more than Simeon, focused on creating a tool kit of techniques. Schools of public policy have moved beyond both critique and technique to estimate risk, ameliorate error and mobilize knowledge. This new agenda requires students of public policy to acquire and employ practical knowledge steeped in the particular and instructed by policy narratives.

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)0.038
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.918

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0190.031
Scholarly communication0.0160.007
Open science0.0020.006
Research integrity0.0120.016
Insufficient payload (model declined to judge)0.0110.002

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 head0.080
GPT teacher head0.447
Teacher spread0.368 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2016
Admission routes2
Has abstractyes

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