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Record W7064413729

Book review - Larry Savage and Charles Smith, Unions in Court: Organized Labour and the Charter of Rights and Freedoms

2018· article· en· W7064413729 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Measurement and Uncertainty Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsCharterFreedom of associationCollective bargainingAusterityCapital (architecture)Supreme courtPower (physics)Labour law
DOInot available

Abstract

fetched live from OpenAlex

The constitutionalization of labour rights in Canada is one of the most remarkable and, perhaps, unexpected developments in the 36 year history of the Charter of Rights and Freedoms. Few observers in 1982 would have predicted that the Charter rights of freedom of expression and association would provide constitutional protection for picket-line activity, collective bargaining, and strikes. Indeed, for some critical observers, the advent of the Charter was viewed as an ominous development, advancing the neo-liberal project of degrading and bypassing democratic institutions to insure the maintenance of conditions favourable to capital accumulation and the power of economic elites. Who better, after all, than the judiciary, the guardians of individual market rights and freedoms, long hostile to collective action by workers, to entrust with this task? However, in recent years, the Supreme Court of Canada (SCC) has provided workers with some cover against the assault of neoliberal governments pursuing austerity measures that restrict collective bargaining and the freedom to strike. How did this happen and what are its implications for the future of the Canadian labour movement? These are some of the questions Savage and Smith set out to answer in this insightful account of the labour movement’s engagement with the Charter and the SCC’s evolving jurisprudence.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.322
Teacher spread0.275 · 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
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
Published2018
Admission routes1
Has abstractyes

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