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Record W2945414449

Rules and Unruliness: Canadian Regulatory Democracy, Governance, Capitalism, and Welfarism

2014· book· en· W2945414449 on OpenAlexaboutno aff
G. Bruce Doern, Michael J. Prince, Richard Schultz

Bibliographic record

Venuenot available
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCapitalismRegulatory stateCorporate governanceWelfarismPoliticsDemocracyPolitical scienceWelfare statePolitical economyPublic administrationState (computer science)Government (linguistics)Economic systemLaw and economicsEconomicsWelfareLawFinance
DOInot available

Abstract

fetched live from OpenAlex

A critical examination of Canadian regulatory governance and politics over the past fifty years, Rules and Unruliness builds on the theory and practice of rule-making to show why government - the inability to form rules and implement structures for compliance - is endemic and increasing. Analyzing regulatory politics and governance in Canada from the beginning of Pierre Trudeau's era to Stephen Harper's government, the authors present a compelling argument that current regulation of the economy, business, and markets are no longer adequate to protect Canadians. They examine rules embedded in public spending programs and rules regarding political parties and parliamentary government. They also look at regulatory capitalism to elucidate how Canada and most other advanced economies can be characterized by co-governance and co-regulation between governments, corporations, and business interest groups. Bringing together literature on public policy, regulation, and democracy, Rules and Unruliness is the first major study to show how and why increasing unruliness affects not only the regulation of economic affairs, but also the social welfare state, law and order, parliamentary democracy, and the changing face of global capitalism.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.465
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.182
Teacher spread0.173 · 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.

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

Citations4
Published2014
Admission routes1
Has abstractyes

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