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Record W3128600163 · doi:10.1177/0020852320988155

Public governance tensions: a managerial artefacts-based view

2021· article· en· W3128600163 on OpenAlexaff
Khouloud Senda Bennani, Anissa Ben Hassine, Bachir Mazouz

Bibliographic record

VenueInternational Review of Administrative Sciences · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsAppropriationCorporate governanceNew public managementContext (archaeology)Public relationsPublic administrationPoliticsMulti-level governancePublic sectorModernization theoryPublic managementSociologyPolitical scienceBusinessEpistemologyLaw

Abstract

fetched live from OpenAlex

The aim of this article is to categorise the factors of tension in public organisational settings. The context of the administrative reforms undertaken in Tunisia has been chosen as an empirical illustration of the public governance tensions associated with managerial artefacts. The study focuses on three types of factors. An analysis of these factors confirms the theories on the appropriation of management tools and helps raise the existing level of knowledge in relation to the processes to mitigate public governance tensions within public organisations. Points for practitioners Today, the modernisation of public governance goes hand in hand with the introduction of new public management tools in administrative settings. On a practical level, the appropriation of these tools generates a tense relationship between political decision-makers and public managers. Often perceived from the perspective of paradoxical demands and antagonistic relationships that disrupt the daily life of state organisations, public governance tensions can be managed as long as they are identified and categorised in the light of the factors of tension associated with the reforms undertaken.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.096
GPT teacher head0.335
Teacher spread0.239 · 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 teacher head, not a consensus.

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

Citations8
Published2021
Admission routes1
Has abstractyes

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