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Record W2089680994 · doi:10.1017/s0008423912000984

Policy Work in Multi-Level States: Institutional Autonomy and Task Allocation among Canadian Policy Analysts

2012· article· en· W2089680994 on OpenAlexaffabout
Michael Howlett, Adam Wellstead

Bibliographic record

VenueCanadian Journal of Political Science · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAutonomyWork (physics)Government (linguistics)Political sciencePublic administrationPrincipal (computer security)Public policyPublic economicsPolicy analysisDistribution (mathematics)EconomicsComputer scienceLaw

Abstract

fetched live from OpenAlex

Abstract. Despite all the attention paid to the topic of policy analysis as a conceptual endeavour, empirically, the actual work of policy analysts is little investigated and little known. This is true generally of most countries and jurisdictions but it is most acute at the subnational level of government in multilevel states. Recent work in Canada, however, based on comprehensive surveys of analysts of provincial and territorial policy, on the one hand, and regionally and Ottawa-based federal policy workers on the other, has found many similarities with national-level work but also significant differences. This work has highlighted differences in the distribution of tasks across jurisdictions—mainly the extent to which policy work involves implementation as well as formulation-related activities—as key distinctions found in policy work across levels of the Canadian multilevel system. This article uses frequency and principal components analysis (PCA) and structural equation modeling (SEM) to probe these dimensions of policy work. It shows provincial and territorial analysts to be similar to regionally based federal workers in task allocation, undermining a straightforward depiction of differences in policy work by level of government. The extent of autonomy enjoyed by policy workers in different jurisdictional venues, both from internal actors and those outside of government, is shown to be the key driver of differences in policy work across levels of government. Résumé. Malgré toute l'attention accordée au thème de l'analyse politique comme un effort conceptuel, empirique du travail réel des analystes des politiques est peu étudié et mal connu. Ceci est vrai en général de la plupart des pays et juridictions, mais est le plus aigu au niveau sous-national de gouvernement dans les États multi-niveaux. Des travaux récents au Canada, cependant, basée sur des enquêtes complètes des provinces et des territoires, d'une part, et régional et basée à Ottawa analystes de la politique fédérale, d'autre part, a trouvé de nombreuses similitudes avec le travail au niveau national mais aussi des différences significatives. Ce travail a mis en évidence des différences dans la répartition des tâches entre les administrations – notamment la mesure dans laquelle le travail politique consiste à la mise en œuvre ainsi que la formulation des activités liées – comme les distinctions clés trouvés dans le travail politique à travers les niveaux de l'canadienne système multi-niveau. Cet article utilise la fréquence et analyse en composantes principales (ACP) et la modélisation par équations structurelles (SEM) pour sonder ces dimensions du travail politique. Il montre les analystes provinciaux et territoriaux à être semblables à l'échelle régionale basée sur les travailleurs fédéraux dans la répartition des tâches, minant une représentation directe des différences dans le travail politique, par niveau de gouvernement. Le degré d'autonomie dont jouissent les travailleurs dans les différents lieux de la politique juridictionnelle – à la fois par des acteurs internes et ceux de l'extérieur du gouvernement – se révèle être le principal moteur de différences dans le travail politique à travers les niveaux de gouvernement.

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)0.007
metaresearch head score (Gemma)0.006
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 score0.296
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0000.002
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 head0.194
GPT teacher head0.465
Teacher spread0.270 · 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

Citations15
Published2012
Admission routes2
Has abstractyes

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