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Record W2972505898 · doi:10.3917/jie.pr1.0047

Multi-Level Issues in Intersectoral Governance of Public Action: Insights from the Field of Early Childhood in Montreal (Canada)

2019· article· en· W2972505898 on OpenAlexaffabout
Angèle Bilodeau, Isabelle Laurin, Carole Clavier, Fabien Rose, Louise Potvin

Bibliographic record

VenueJournal of Innovation Economics & Management · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsCentre de Santé et de Services Sociaux CavendishUniversité du Québec à MontréalInstitut National de Santé Publique du QuébecUniversité de Montréal
Fundersnot available
KeywordsStatus quoCorporate governanceAccountabilityAction (physics)Political sciencePublic administrationPublic relationsField (mathematics)Business

Abstract

fetched live from OpenAlex

Putting societal issues on the agenda of public action calls for advanced forms of collaboration between sectors and levels of governance. However, action systems have multiple silos, both horizontal and vertical, that impede collaboration. Therefore, clarifying the challenges of intersectoral and multi-level governance becomes highly relevant. Based on the three-I approach, a study of early childhood programs in Montreal highlights these issues. The study identifies various sectoral mechanisms and rules at the provincial level that hinder innovation in regional and local intersectoral action systems. Compartmentalized accountability by program appears to be the most constraining rule in favour of the status quo. The study illustrates how the local level can be both the place for reproducing sectorization and the ideal place for intersectoral coordination. JEL Codes: I18

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)0.002
metaresearch head score (Gemma)0.006
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 score0.748
Threshold uncertainty score0.867

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0220.013
Scholarly communication0.0080.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.037
GPT teacher head0.291
Teacher spread0.254 · 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

Citations5
Published2019
Admission routes2
Has abstractyes

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