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Record W4293446679 · doi:10.1111/capa.12493

The digital era and public sector reforms: Transformation or new tools for competing values?

2022· article· en· W4293446679 on OpenAlexaffabout
Evert A. Lindquist

Bibliographic record

VenueCanadian Public Administration · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPublic valuePublic sectorNew public managementCorporate governancePublic serviceDigital transformationPublic administrationValue (mathematics)Political sciencePublic relationsBusinessEconomicsManagementEconomyComputer science

Abstract

fetched live from OpenAlex

Abstract This article considers the tools and management approaches associated with the “digital‐era” public sector reform, which many observers suggest has supplanted or should supplant previous reforms such as those associated with the New Public Management. This article levers and adapts the Competing Values Framework to categorize various public service reform movements—Traditional Public Administration, New Public Management, Public Value Management, and New Public Governance—and associated value systems and cultures. It argues that not only do these prior reform movements persist as values and repertoires in public service systems, but they are also each variously receiving oxygen from “digital” as the latest wave of technological innovation affecting societies, markets, and governments. It calls for more systematic empirical work to gauge how digital tools have been affecting the mix and balance of values and repertoires associated with these reform movements in different parts of public service systems in Canada and beyond.

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)0.013
metaresearch head score (Gemma)0.019
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 score0.947
Threshold uncertainty score0.387

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0070.093
Scholarly communication0.0250.019
Open science0.0020.011
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.000

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 head0.099
GPT teacher head0.352
Teacher spread0.252 · 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

Citations51
Published2022
Admission routes2
Has abstractyes

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