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Record W7161980459 · doi:10.82308/41983

Lessons from India's constitutional culture: what Canada can learn

2013· dissertation· en· W7161980459 on OpenAlexaboutno aff
Isabelle Gilles

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsJurisprudenceSupreme courtIdeologyFundamental rightsInternational human rights lawEconomic JusticeSocial rights

Abstract

fetched live from OpenAlex

This thesis aims at initiating dialogue between Canadian and Indian constitutional cultures. Canadian constitutional law is arguably characterized by ideologies of liberalism and legal positivism. Because human rights norms are expected to incorporate a vision of social justice into the law, ideologies and legal philosophies are crucial to assess the potential and the limitations of human rights protections. The legal cultures in Canada and India have similar roots, and yet the systems have evolved differently. Among other factors, judicial activism and the quest for social justice of judges at the Supreme Court of India were significant in the evolution of Indian constitutional culture. From a Canadian perspective, it is interesting to study this culture as it offers new avenues in the human rights field and therefore challenges the universal value of human rights norms as interpreted and applied in Canada. This thesis argues that, on the intersection of human rights and social issues like poverty and social classes, important lessons can be drawn from the way the Supreme Court of India has based its human rights interpretation on contextual analyses of Indian social reality. The judges engaged in judicial activism sought to move beyond the traditional ideologies found in the common law, and their jurisprudence is helpful in grasping the limitations these ideologies can put on human rights interpretation.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.097
Threshold uncertainty score0.706

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.007
Science and technology studies0.0210.014
Scholarly communication0.0160.008
Open science0.0020.005
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0110.001

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 head0.024
GPT teacher head0.312
Teacher spread0.288 · 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 designTheoretical or conceptual
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
Published2013
Admission routes1
Has abstractyes

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