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Public sector performance and decentralization of decision rights

2012· article· en· W1949638111 on OpenAlexaff
Benoit A. Aubert, Simon Bourdeau

Bibliographic record

VenueCanadian Public Administration · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsDecentralizationDelegationAccountabilityAutonomyPublic sectorPrivate sectorMargin (machine learning)BusinessOutcome (game theory)Public economicsControl (management)Organizational performanceEconomicsPublic administrationPolitical scienceMicroeconomicsEconomic growthMarketingMarket economyComputer scienceManagement

Abstract

fetched live from OpenAlex

Abstract In recent years, governments have introduced several reforms, often adopting management mechanisms traditionally associated with the private sector. This article looks specifically at the impact of decision‐rights decentralization, along with accountability mechanisms, on performance. Twenty public sector organizations, experiencing a shift from rule‐based to outcome‐based control mechanisms and benefiting from different levels of autonomy and decision margins, were studied. Results show a link between the degree of power delegation and increased organizational performance. The units benefiting from greater freedom with respect to financial and human resources decisions experienced the greatest margin of performance increase. These findings underline the importance of considering the level at which measures are defined and the elements included in the measurement mechanisms (outcomes or rules).

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)0.011
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.049
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.203
Teacher spread0.179 · 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 designObservational
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

Citations13
Published2012
Admission routes1
Has abstractyes

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