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Record W2809729443 · doi:10.1080/15236803.2018.1473024

Critical gaps in public policy programs in Canada: Identifying subject areas for graduate training in rural policy

2018· article· en· W2809729443 on OpenAlexafffundabout
Gary McNeely, William Ashton

Bibliographic record

VenueJournal of Public Affairs Education · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsBrandon University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCourseworkPublic policyPublic administrationPolicy analysisPolitical sciencePolicy studiesSubject (documents)PoliticsRural areaEducation policyRural managementEconomic growthPublic relationsRural developmentHigher educationSociologyPedagogyEconomicsLibrary scienceGeography

Abstract

fetched live from OpenAlex

Since rural Canada contributes about one-third of the national economy and significantly to culture, we argue it deserves greater attention in public policy, beginning with policy education. A scan of 22 Canadian Masters of Public Policy (MPP) and Master of Public Administration (MPA) programs reveals a marked absence of policy training focused on rural issues and communities. By comparing the subject areas offered in these programs and the learning outcomes presented at the 2015 International Comparative Rural Policy Studies (ICRPS) summer institute, we identify subject areas essential for training in rural policy. The comparison establishes an important congruence in the learning offered in the MPP/MPA programs and the summer institute, and yet critical differences. The analysis recognizes that training in analytical tools and socio-political contexts is foundational for policy design and implementation. However, acquiring competency in rural policy reinforces the need for graduate coursework centred on rural policy sectors.

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)0.011
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.368
GPT teacher head0.507
Teacher spread0.139 · 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 designObservational
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
Published2018
Admission routes3
Has abstractyes

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