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Record W2017758746 · doi:10.1504/ijitm.2007.013998

Electronic service delivery in a multi-channel public sector: an assessment of the government of Canada

2007· article· en· W2017758746 on OpenAlexaffabout
Jeffrey Roy

Bibliographic record

VenueInternational Journal of Information Technology and Management · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsDalhousie University
Fundersnot available
KeywordsService delivery frameworkGovernment (linguistics)Service (business)BusinessCorporate governancePublic sectorRelevance (law)Service designOrder (exchange)Public relationsPublic administrationPrivate sectorMarketingPolitical scienceEconomicsEconomic growthFinance

Abstract

fetched live from OpenAlex

The purpose of this article is to provide a critical assessment of both the Canadian federal government's experience to date with online service delivery and the prospects for Service Canada, a new vehicle for government-wide service transformation. In doing so, our primary interest lies in better understanding the organisational dimensions to this transformation and the extent to which these dimensions are both addressed in and well aligned with the federal government apparatus. Upon review of the background to and creation of Service Canada, five major sets of factors are adopted in order to analyse the prospects for this government-wide service transformation initiative. They include the relevance and management of a multi-channel service apparatus; the governance architecture; the importance of management by networks; public-private partnering; and senior management and political support. The article concludes that significant effort is required in each of these areas in order for Service Canada to succeed.

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)0.010
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 score0.175
Threshold uncertainty score0.957

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.012
Science and technology studies0.0100.003
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.010
GPT teacher head0.277
Teacher spread0.267 · 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

Citations11
Published2007
Admission routes2
Has abstractyes

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