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Record W7099289942

The Canadian Political Science Association

2005· article· en· W7099289942 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPharmacological Effects of Medicinal Plants
Canadian institutionsnot available
Fundersnot available
KeywordsMargin of appreciationCharterParallelsConventionHuman rightsVetoPolitics
DOInot available

Abstract

fetched live from OpenAlex

By empowering judges to establish national standards, the Charter of Rights limits the capacity of provincial governments to build distinctive communities. But, Samuel LaSelva reminds us, if the Charter is to be the “nation-saving ” device it purports to be, we require a conception of Charter rights that not only acknowledges this will to live together, but recognizes our desire to live apart. Despite this imperative, scant judicial attention has been paid to developing a consistent model of rights in a federal context. Such is not the case in the Council of Europe, where judges have gone to great lengths to articulate a conception of the European Convention on Human Rights that is cognizant of member states ’ desire to maintain distinctive national communities. This recognizes the diverse cultural and legal experiences of the Member States as legitimate justification for the limitation of, or deviation from, otherwise pan-European standards. Despite important parallels with the Canadian context, however, the margin of appreciation has been virtually ignored by Canadian scholars and jurists alike. This paper corrects this oversight, and explores the margin of appreciation in the Canadian context. Following some necessary background on “the margin, ” as well as an exploration of the ultimately misguided application of the principle in Canada to date, it concludes that the margin of appreciation may, with necessary modification, be a particularly appropriate way of thinking about the relationship between federalism and the Charter.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
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 head0.016
GPT teacher head0.342
Teacher spread0.326 · 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
Published2005
Admission routes1
Has abstractyes

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