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Economic and Social Rights Across Time, Regions, and Legal Traditions: A Preliminary Analysis of the TIESR Dataset

2012· article· en· W114679256 on OpenAlexaff
Courtney Jung, Evan Rosevear

Bibliographic record

VenueNordic Journal of Human Rights · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocial rightsPolitical scienceLawInternational human rights lawReservation of rightsNorm (philosophy)Human rightsFundamental rightsRight to property

Abstract

fetched live from OpenAlex

Nearly all written constitutions in the developing world contain one or more economic and social rights. However, some rights are more commonly enshrined than others, and there is wide variation in terms of whether such rights are identified as justiciable – enforceable in a court of law – or merely aspirational. The most interesting variations occur along three dimensions: time, region, and legal tradition. Most constitutions are new, and the contemporary constitutional model affords greater standing to economic and social rights than the previous post-War model. There are significant regional differences in the relative prevalence of such rights, and some regions exhibit a clear regional norm with respect to economic and social rights. Finally, the constitutions of common law countries are significantly less likely to include economic and social rights, and to identify them as justiciable, than those of civil law countries. This article reports some of the initial findings of a new dataset measuring the constitutional entrenchment of economic and social rights. Keywords Economic Rights Social Rights Constitutions.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.005

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 head0.028
GPT teacher head0.314
Teacher spread0.286 · 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 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

Citations1
Published2012
Admission routes1
Has abstractyes

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