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Record W2165985467 · doi:10.1111/capa.12055

<scp>A</scp>demographic and career profile of municipal<scp>CAOs</scp>in<scp>C</scp>anada: Implications for local governance

2014· article· en· W2165985467 on OpenAlexaffabout
Patrick Eamon O'Flynn, Tim A. Mau

Bibliographic record

VenueCanadian Public Administration · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of Guelph3M (Canada)
Fundersnot available
KeywordsHumanitiesPolitical scienceOfficerRepresentativeness heuristicSociologyArtPsychologySocial psychologyLaw

Abstract

fetched live from OpenAlex

Abstract Little has been written about the Canadian municipal chief administrative officer ( CAO ). Given the growing importance of municipalities, it is an important gap in the literature. This article depicts CAOs as a group of older white men, highly educated and very experienced. Given the impending retirement of a significant number of CAOs , municipalities have an opportunity to achieve a greater degree of representativeness. The authors identify a number of research trajectories to enhance our knowledge and understanding of this critical administrative position. Sommaire Il existe peu de documents sur les chefs des services municipaux au C anada. Étant donné l'importance croissante des municipalités, cela représente une grave lacune dans la documentation. Cet article décrit les chefs des services municipaux comme un groupe d'hommes blancs d'un certain âge, ayant un haut niveau d'études et une grande expérience. Comme un grand nombre d'entre eux vont prendre leur retraite d'ici peu, les municipalités ont une occasion de parvenir à un plus grand degré de représentativité. Les auteurs identifient un certain nombre de pistes de recherche pour améliorer nos connaissances et notre compréhension de cette fonction administrative cruciale.

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)0.002
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.045
GPT teacher head0.319
Teacher spread0.274 · 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 designNot applicable
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

Citations10
Published2014
Admission routes2
Has abstractyes

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