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Record W2478430076 · doi:10.4018/ijpada.2016100104

Mobility and Service Innovation

2016· article· en· W2478430076 on OpenAlexaffabout
Jeffrey Roy

Bibliographic record

VenueInternational Journal of Public Administration in the Digital Age · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGovernment (linguistics)Public sectorPublic servicePublic valueBusinessPublic administrationCorporate governanceService delivery frameworkService (business)New public managementPublic relationsValue (mathematics)Administration (probate law)Political scienceMarketingFinance

Abstract

fetched live from OpenAlex

This article examines the Canadian public sector's efforts to devise mobile service capacities predicated upon efficiency, engagement, and innovation, and how such capacities are intertwined with both the advent of Gov 2.0 and the inertia of traditional public administration. The author's primary focus is on the federal government (Government of Canada), with some additional consideration of provincial governments and inter-governmental dynamics as appropriate. Through three typologies of public sector governance (traditional public administration, new public management, and public value management), the author seeks to better understand these aforementioned tensions – and formulate fresh insights into how governments can pursue the leveraging of mobility as a basis for not only more efficient service delivery but also wider opportunities for public engagement and service innovation.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.151
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0070.026
Scholarly communication0.0080.005
Open science0.0010.007
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.047
GPT teacher head0.336
Teacher spread0.289 · 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 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

Citations1
Published2016
Admission routes2
Has abstractyes

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