MétaCan
Menu
Back to cohort
Record W4385511820 · doi:10.60082/2563-4631.1093

International Accountability in the Implementation of the Right to Development and the “Wonderful Artificiality” of Law: An African Perspective

2020· article· en· W4385511820 on OpenAlex
Obiora Chinedu Okafor, Uchechukwu Ngwaba

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Transnational Human Rights Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsBC Innovation CouncilYork University
Fundersnot available
KeywordsAccountabilityRight to developmentTransformative learningArtificialityPolitical scienceHuman rightsContext (archaeology)State (computer science)Perspective (graphical)LawLaw and economicsSociologyPedagogyEpistemology

Abstract

fetched live from OpenAlex

The landscape for the implementation of the right to development has undergone significant transformative shifts with the recent establishment of a new expert mechanism on the right to development by the UN Human Rights Council, and the finalisation of a draft treaty on the right to development. Yet, much more can clearly still be done to strengthen UN, state and non-state actors thinking on accountability in the implementation of the right to development, to add to the already considerable progress that has taken place. Our paper explores what can be done, focusing on the African and international context. We conclude that by reflecting on the benefits which a greater focus on accountability in UN development thinking post-2015 can bring to the table, the chances of success of the right to development is heightened.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.387
Teacher spread0.323 · 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