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Governmental policy capacity and policy work in a small place: Reflections on perceptions of civil servants in Prince Edward Island, Canada from a practitioner in the field

2020· article· en· W3118602976 on OpenAlexaffabout
Bobby Thomas Cameron

Bibliographic record

VenueJournal of Public Administration Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsCivil servantsInsiderPublic administrationPoliticsContext (archaeology)Work (physics)Public relationsPolitical sciencePublic policyPerceptionAdministration (probate law)SociologyLawGeographyPsychologyEngineering

Abstract

fetched live from OpenAlex

The body of public administration literature is missing contributions from practitioners in the field. Emic or insider-led studies of public administration can act as powerful mechanisms to generate new knowledge. This article studies the relationship between place, perceptions and policy work by drawing on the author’s own public administration experience and interviews with civil servants. The results show that societal factors such as political culture and reduced anonymity associated with small place create challenges when developing public policy. However, expedited public engagement and problem identification were perceived by civil servants to be enhanced by a small context. This means that small place can be both limiting and beneficial for high levels of policy capacity. Overall, this article finds that geospatial factors such as smallness impact perceptions of policy work and capacity. Furthermore, this article finds that insider-led studies of public administration can indeed make important and unique contributions to the body of literature and are therefore deserving of more serious methodological consideration.

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)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.134
GPT teacher head0.430
Teacher spread0.296 · 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 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

Citations6
Published2020
Admission routes2
Has abstractyes

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