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Record W2214428410

Book Review: Federalism, Democracy and Disability Policy in Canada

2004· article· en· W2214428410 on OpenAlexaffabout
Dianne Pothier

Bibliographic record

VenueSSRN Electronic Journal · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFederalismPublic administrationPolitical scienceFederalistDemocracyDemocratic deficitCriticismPublic policyGovernment (linguistics)Multi-level governanceCorporate governanceLawPoliticsEconomicsManagement
DOInot available

Abstract

fetched live from OpenAlex

FEDERALISM, DEMOCRACY AND DISABILITY POLICY IN CANADA, edited by Alan Puttee, was published in 2002 for the Institute of Intergovernmental Relations, Queen's University School of Policy Studies, as the fifth contribution in a six volume Union Series. The project began in 1997, and was ongoing at the February 4, 1999, signing of the Social Union Framework Agreement (SUFA) between the Canadian federal government and all of the provinces except Quebec. One might have expected, therefore, that SUFA would be a prime focus of this book. The fact that it is not is, I think, more of a comment on the failings of SUFA than a criticism of the book. The reality is that SUFA has not had a significant impact on public policy in Canada, whether on disability policy or otherwise. Instead of an analysis of the SUFA as it relates to disability policy, this book provides a general survey of disability policy in Canada, with attention to the history of how it has unfolded. The authors were given the tasks of assessing the impact of present and possible governance structures on: (1) meeting policy objectives; (2) reflecting democratic values; and, (3) respecting federalist principles.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.179
Threshold uncertainty score0.360

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.021
Science and technology studies0.0030.002
Scholarly communication0.0050.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0240.005

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 head0.006
GPT teacher head0.275
Teacher spread0.269 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2004
Admission routes2
Has abstractyes

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