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Record W4297271212 · doi:10.1111/capa.12486

The time management styles of deputy ministers in Canada: Towards a taxonomy

2022· article· en· W4297271212 on OpenAlexaffabout
Patrice Dutil, Andrea Migone

Bibliographic record

VenueCanadian Public Administration · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGovernment (linguistics)Public administrationPolitical scienceWork (physics)PerceptionTaxonomy (biology)Position (finance)ManagementPublic relationsPsychologyBusinessEngineeringEconomics

Abstract

fetched live from OpenAlex

Abstract Deputy Ministers in Canada play a critical role in defining all aspects of policy and operations for their respective departments and also contribute significantly to their government's collective initiatives. While there exists a solid understanding of the roles and functions of Deputy Ministers, there is little perception of how individuals actually shape the position in terms of time management. Understanding how senior executives in Canadian public service organize their time is critical to an appreciation of how they prioritize their functions. Using results from a 2020 survey, this study documents how Deputy Ministers in Canada divide their weeks in terms of personal work and meetings. This study goes further to discern a taxonomy of five time‐allocation styles of Deputy Ministers: Operational, Balanced, Managerial, Strategic and HR‐Focused.

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)0.004
metaresearch head score (Gemma)0.012
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 score0.066
Threshold uncertainty score0.482

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.009
Science and technology studies0.0090.006
Scholarly communication0.0070.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.290
Teacher spread0.245 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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