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Privatizing Responsibility: Public Sector Reform under Neoliberal Government

2009· article· fr· W2035517411 on OpenAlexaffabout
Suzan Ilcan

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2009
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCitizenshipPublic administrationPrivate sectorPublic sectorGovernment (linguistics)Neoliberalism (international relations)Public servicePolitical scienceNew public managementPoliticsLaw

Abstract

fetched live from OpenAlex

À la lumière des réformes du secteur public au Canada et ailleurs, l'auteure se concentre sur le déplacement des priorités des responsabilités sociales vers les responsabilités privées et soulève de nouvelles questions sur les forces de l'entreprise privée et des partenariats reposant sur les mécanismes du marché. D'après elle, dans les programmes néo‐libéraux du gouvernement, laresponsabilité de la privatisationest liée à trois principaux aménagements : la reconsidération des relations entre le public et le privé; la mobilisation de l'esprit de civisme; et la création d'une mentalité culturelle d'observance de la règle allant de pair avec ces transformations. La recherche qui a été effectuée pour cet article s'est fondée sur l'analyse à grande échelle de documents de politique et d'initiatives de réforme du secteur public, de même que sur des interviews de fonctionnaires fédéraux canadiens. In light of public sector reforms in Canada and elsewhere, this paper focuses on the shift of emphasis from social to private responsibilities and raises new questions about the forces of private enterprise and market‐based partnerships. Under neoliberal governmental agendas,privatizing responsibilitylinks to three main developments: the reconsideration of the relations of public and private; the mobilization of responsible citizenship; and the formation of a cultural mentality of rule that works alongside these developments. The research for this article is based on extensive analysis of policy documents and public sector reform initiatives, as well as interviews with Canadian federal public service employees.

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)0.007
metaresearch head score (Gemma)0.009
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 score0.826
Threshold uncertainty score0.958

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.035
Scholarly communication0.0100.005
Open science0.0010.008
Research integrity0.0020.004
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 head0.089
GPT teacher head0.307
Teacher spread0.218 · 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

Citations157
Published2009
Admission routes2
Has abstractyes

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