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Record W3031400100 · doi:10.3138/9781487517076

Federalism in Action: The Devolution of Canada's Public Employment Service, 1995-2015

2018· book· en· W3031400100 on OpenAlexaboutno aff
Donna E. Wood

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2018
Typebook
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDevolution (biology)FederalismPublic administrationAction (physics)Political sciencePublic serviceService (business)SociologyEconomicsLawPoliticsEconomy

Abstract

fetched live from OpenAlex

Every developed country has a public employment service that connects job seekers with employers through information, placement, and training support services. In Federalism in Action, Donna E. Wood assesses how Canada’s public employment service is performing after responsibility was transferred from the federal government to provinces, territories, and Aboriginal organizations between 1995 and 2015. Drawing upon over twenty years of data, Wood reveals the governance choices provinces made, the reasons behind these choices, and the outcomes they achieved. Provincial decisions regarding employment programming is an important public policy issue about which little is known, and even less understood within the context of Aboriginal communities. Federalism in Action includes analytical comparisons of Canada’s employment programming with the United States, Australia, and the European Union, as well as information from insightful interviews with key informants from every province. In firmly placing Canada within the extensive international literature on the governance of welfare-to-work policies, this book makes an important new contribution to research

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.783
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.038
GPT teacher head0.258
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2018
Admission routes1
Has abstractyes

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