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The “lumpiness” thesis revisited: the venues of policy work and the distribution of analytical techniques in Canada

2018· book-chapter· en· W4236837390 on OpenAlexaboutno aff
Michael Howlett, Seck Tan, Adam Wellstead, Andrea Migone, Bryan Evans

Bibliographic record

VenuePolicy Press eBooks · 2018
Typebook-chapter
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Distribution (mathematics)Work (physics)Process (computing)Public policyPublic economicsPolicy analysisPolitical scienceBusinessRegional sciencePublic administrationEconomicsGeographyEngineeringComputer scienceMathematics

Abstract

fetched live from OpenAlex

This chapter contributes to the understanding of analytical practices and tools employed by policy analysts involved in policy formulation and appraisal by examining data drawn from 15 surveys of federal, provincial and territorial government policy analysts in Canada conducted in 2009-2010, two surveys of NGO analysts conducted in 2010-2011 and two surveys of external policy consultants conducted in 2012-2013. Data from these surveys allows the exploration of several facets of the use of analytical tools, ranging from more precise description of the frequency of use of specific kinds of tools and techniques in government to their distribution between permanent government officials and external policy analysts. As the chapter shows, the frequency of use of major types of analytical techniques used in policy formulation is not the same between the three types of actors and also varies within government by Department and issue type. Nevertheless some general patterns in the use of policy appraisal tools in government can be discerned, with all groups employing process- related tools more frequently than ‘substantive’ tools related to the technical analysis of policy proposals.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.130
GPT teacher head0.428
Teacher spread0.298 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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