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Record W4412890558 · doi:10.1080/25741292.2025.2539569

Collaborative public sector innovation in a post-NPM era: a design perspective

2025· article· en· W4412890558 on OpenAlexaffabout
Mattia Casula, Andrea Migone

Bibliographic record

VenuePolicy Design and Practice · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPerspective (graphical)Public sectorPolitical scienceSociologyRegional scienceBusinessComputer science

Abstract

fetched live from OpenAlex

Despite the recent advancements in the literature on Collaborative Public Sector Innovation (CPSI), several weaknesses persist, particularly regarding our understanding of CPSI as a policy design issue. This article addresses the complex nature of CPSI in a post-NPM world, looking at a Canadian childcare innovation project and highlighting the complex interaction of collaboration, external pressures, and public service design. Our analysis recognizes diversity as a vital component of CPSI, highlighting that different methods exist for implementing and conceptualizing this collaboration. A key factor for the success of CPSI is the ability of organizations to strategically connect innovation with design processes. We emphasize that the likelihood of successful innovation increases when organizations reconsider project design and adopt an outcome-oriented strategy, rather than an output-focused process that fails to engage with broader system thinking and design principles. However, this does not automatically mean that the broadest collaboration and co-design approach is followed.

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)0.056
metaresearch head score (Gemma)0.034
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: none
Teacher disagreement score0.056
Threshold uncertainty score0.294

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0110.044
Scholarly communication0.0230.013
Open science0.0050.015
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.051
GPT teacher head0.324
Teacher spread0.273 · 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
Published2025
Admission routes2
Has abstractyes

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