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Record W2036512621 · doi:10.1080/14719037.2013.867066

Environmental Determinants of Public Sector Innovation: A study of innovation awards in Canada

2014· article· en· W2036512621 on OpenAlexafffundabout
Luc Bernier, Taı̈eb Hafsi, Carl Deschamps

Bibliographic record

VenuePublic Management Review · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsHEC MontréalÉcole Nationale d'Administration Publique
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPublic sectorGovernment (linguistics)Investment (military)Public managementTertiary sector of the economyEmpirical researchPublic servicePublic administrationBusinessUnemploymentPublic policyEconomicsPublic economicsAccountingPolitical scienceEconomic growthEconomyMarketing

Abstract

fetched live from OpenAlex

In this article, we conduct an empirical study of administrative innovation in the Canadian public sector by examining applications to the Innovative Management Award of the Institute of Public Administration of Canada (IPAC). After a review of the literature on innovation in the public sector and of the history of this award, we come to the conclusion that the relationship between innovation and environment has been studied only sparingly, which explains the focus of our research and our hypotheses. Through an analysis of award applications over 21 years, and of award finalists and winners, we demonstrate that such environmental variables as strength of the economy, size of the civil service, deficits, unemployment rate, investment in R&D, and type of government have important consequences for administrative innovation in the public sector. We also suggest some implications of our findings for future research on this subject.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.060
Threshold uncertainty score0.436

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.010
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.234
Teacher spread0.208 · 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 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

Citations76
Published2014
Admission routes3
Has abstractyes

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