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Record W2028482893 · doi:10.1080/13876988.2014.952530

Federal Dynamics of Changing Governance Arrangements in Education: A Comparative Perspective on Australia, Canada and Germany

2014· article· en· W2028482893 on OpenAlexaboutno aff
Giliberto Capano

Bibliographic record

VenueJournal of Comparative Policy Analysis Research and Practice · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceAccountabilityOrder (exchange)Political sciencePerspective (graphical)Field (mathematics)Public administrationEconomicsManagement

Abstract

fetched live from OpenAlex

Education policy is a highly interesting field from the point of view of governance, given the substantial changes that have been made throughout the world to the governance of such policy over the last 30 years or so. Western governments in particular have made significant changes in the governance arrangements of their education policy in order to achieve two fundamental goals: increased efficiency and greater accountability. In this process, the role of governments has changed but not diminished. This paper explores such developments by comparing the trajectories of governance reforms in three federal countries (Australia, Canada and Germany). What emerges is that the role of governments is key to all governance mixes modelled by the reform processes in the three analysed countries, and that there is greater “national” coordination than before, but also significant differences in the strategies adopted and in the content of reform, due to the differing nature of such countries’ federal dynamics.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.753

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0070.005
Scholarly communication0.0050.002
Open science0.0010.002
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 head0.124
GPT teacher head0.511
Teacher spread0.387 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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