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Record W2430017036

Federalism and the knowledge economy: The shifting contours of higher education policy in Canada and Germany

2016· article· en· W2430017036 on OpenAlexaboutno aff
Gangolf Braband, Robert Harmsen

Bibliographic record

VenueOpen Repository and Bibliography (University of Luxembourg) · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsnot available
Fundersnot available
KeywordsFederalismKnowledge economyPolitical sciencePolitical economyEconomyEconomicsLawPolitics
DOInot available

Abstract

fetched live from OpenAlex

The present paper addresses the question of the extent to which the emergence of a ‘knowledge economy’ or ‘knowledge society’ may be seen as reshaping the contours of responsibility for higher or post-secondary education in federal systems. It addresses this question through a comparative study of Canada and Germany, framed within an understanding of both the persistence of distinctive federal models and of the emergence of more complex structures of multi-level governance. Empirically, attention is focused on the emergence of comparable federal strategies of dis- and re-engagement with the higher education sector, producing a focus in both cases on ‘research excellence’ initiatives. A picture emerges of a broadly convergent sectoral agenda, but in which distinctive national institutional systems continue to shape distinctive policy responses. The German case is distinguished by both the stronger horizontal dimension of the federal system and its placement within the wider European context (notably the Bologna Process). Conversely, the Canadian case is distinguished by the direct influence which (major research) universities themselves are able to exercise as political actors. The study draws on extensive documentary research and interviews in the two countries, at both national and sub-national level.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.591
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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