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Record W4389310042 · doi:10.1017/s0008423923000598

Ministerial Mandate Letters and Co-ordination in the Canadian Executive

2023· article· en· W4389310042 on OpenAlexaffabout
Kenny William Ie

Bibliographic record

VenueCanadian Journal of Political Science · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMandateCabinet (room)Public administrationGovernment (linguistics)Agency (philosophy)IncentivePrime ministerPolitical sciencePrime (order theory)OrdinationPublic relationsPoliticsLawEconomicsSociologyEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract Prime ministers in parliamentary systems confront a challenging agency problem in leading cabinets toward cross-government priorities: ministers tend to prioritize departmental interests and may lack incentives and/or information enabling co-ordinated effort. In Canada, a novel mechanism for both increasing incentives and information provision has been developed in recent decades: the mandate letter. These letters are issued by Canadian prime ministers to their ministers, reinforcing government priorities, each minister's responsibilities, and specific policy expectations. This article examines mandate letters as mechanisms inducing interministerial policy co-ordination, focusing on the 2015–2021 period, under Justin Trudeau, as the first Canadian prime minister to release these letters publicly. Using topic modelling and social network analysis, I find that Trudeau has increasingly sought to strengthen ministerial co-ordination and ministers’ focus on crosscutting policy priorities. This case study contributes to our understanding of intraexecutive co-ordination and the agency problem in cabinet government.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.087
Threshold uncertainty score0.634

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0110.004
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.054
GPT teacher head0.369
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes2
Has abstractyes

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