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Record W2037599414 · doi:10.1111/capa.12103

What metrics? On the utility of measuring the performance of policy research: An illustrative case and alternative from Employment and Social Development Canada

2015· article· en· W2037599414 on OpenAlexaffabout
Edward Nason, Michael O’Neill

Bibliographic record

VenueCanadian Public Administration · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of OttawaInstitute on Governance
Fundersnot available
KeywordsGovernment (linguistics)Task (project management)Object (grammar)Performance measurementFocus (optics)Computer scienceResearch ObjectState (computer science)Policy developmentPublic policyManagement scienceOperations researchPublic economicsEconometricsEconomicsSociologyEngineeringPolitical sciencePublic administrationArtificial intelligenceRegional scienceEconomic growthManagement

Abstract

fetched live from OpenAlex

Abstract This article examines the state of performance measurement of policy research in government. The article observes that, to date, government policy research activities have seldom been the object of performance measurement, a factor we ascribe to the relative unsuitability of existing models rooted in a focus on outputs and outcomes, often at the expense of relationships and networks. In reference to the literature and the case study, the article proposes that existing performance measurement models are ill‐suited to the task of assessing policy research performance. As a result, the article proposes that a purpose‐built model may be needed to achieve this objective. Such a model, the Sphere of Influence of Research Policy model, is provided as an illustration.

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)0.006
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.647
GPT teacher head0.500
Teacher spread0.147 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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