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Digital Public Intrapreneurship and Digital Public Entrepreneurship

2024· book-chapter· en· W4394834748 on OpenAlexaff
Maxime Cuillerier

Bibliographic record

VenueAdvances in electronic government, digital divide, and regional development book series · 2024
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsÉcole Nationale d'Administration Publique
Fundersnot available
KeywordsEntrepreneurshipIntrapreneurshipPublic serviceTransformative learningPublic sectorPublic relationsAdaptabilityPolitical scienceBusinessKnowledge managementSociologyManagementEconomicsComputer science

Abstract

fetched live from OpenAlex

This chapter provides an examination of the landscape of entrepreneurship within public administration, focusing on the integration and implications of digital technologies. It begins with the historical evolution of public sector entrepreneurship, identifying milestones and shifts towards more inclusive and innovative practices. The chapter transitions to the emergent field of digital entrepreneurship and intrapreneurship, underscoring the transformative potential of digital technologies in public organizations. The chapter presents concepts, theoretical frameworks, and definitions for understanding the dynamics of digital entrepreneurship within the public sector. Emphasis is placed on the roles of digital public entrepreneurs and intrapreneurs, whose efforts are pivotal in navigating challenges and seizing the opportunities from the digital era. The chapter concludes by presenting the strategic importance of fostering a culture of innovation and adaptability within public institutions, aiming to enhance service delivery and public value creation in an increasingly digital world.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.027
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0110.010
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0270.005

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 head0.015
GPT teacher head0.196
Teacher spread0.181 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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