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Record W2530981181 · doi:10.1080/21699763.2016.1237373

Do constitutions guarantee equal rights across socioeconomic status? A half century of change in the world's constitutions

2016· article· en· W2530981181 on OpenAlexfundno aff
Adèle Cassola, Amy Raub, Jody Heymann

Bibliographic record

VenueJournal of International and Comparative Social Policy · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsnot available
FundersCanada Research ChairsCanada Foundation for InnovationBill and Melinda Gates Foundation
KeywordsSocioeconomic statusDisadvantagedConstitutionPoliticsPolitical sciencePovertyVotingInequalityInclusion (mineral)Economic growthDemographic economicsDevelopment economicsLawSociologyEconomicsSocial scienceDemography

Abstract

fetched live from OpenAlex

For those disadvantaged by bias and barriers based on socioeconomic status (SES), constitutions can provide a defense against discrimination and a foundation for greater equality in social, economic, and political life. In light of the near-global commitment to a multi-dimensional poverty reduction agenda and the increased inclusion of marginalized groups in constitution-drafting processes, this article examines how 193 constitutions address SES and how this has changed over time. The majority of constitutions guarantee equal access to primary education across SES (59%) and prohibit discrimination on this basis (58%). Fewer guarantee access to healthcare (20%), equal rights in employment (15%), eligibility for legislative office (4%), and voting rights (4%) across SES. Constitutions adopted after 1990 are considerably more likely to protect equal rights across SES than older ones. However, 25% of constitutions – including 17% of those adopted since 1990 – restrict political participation based on socioeconomic characteristics.

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)0.029
metaresearch head score (Gemma)0.072
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.072
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.022
Scholarly communication0.0080.015
Open science0.0010.005
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.124
GPT teacher head0.428
Teacher spread0.304 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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