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Record W2787006752

The Role of the United Nations Environmental Programme (UNEP) to the Development of International Environmental Law

2017· article· en· W2787006752 on OpenAlexaboutno aff
Hakeem Ijaiya

Bibliographic record

VenueKIU Journal of Social Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Environmental Law and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsMandateEnvironmental lawNegotiationPolitical scienceMontreal ProtocolConventionIncentiveUnited Nations Convention on the Law of the SeaEnvironmental protectionLawBusinessPublic administrationGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

The United Nations Environmental Programme (UNEP) is one of the prominent United Nation (UN) organs and bodies which address environmental protection issues. UNEP has been described as the environmental conscience of the UN System. UNEP has also been recognized as a major catalytic instrument for global environmental co-operation. Since its inception in 1972, UNEP has formulated scientific position, develop legal strategies and build political support to develop binding agreements (such as the Montreal Protocol, 1989, Basel Convention, 1989 etc) and nonbinding agreements. The focus of this paper is on UNEP’s birth, mandate, and contribution in the development of international environmental law. The study examines the role of UNEP as a forum for negotiations on environmental problems that fall outside the mandate of other intergovernmental organizations. The study relies on primary and secondary sources of information. The information obtained through these sources was subjected to content analysis. The study found that UNEP did not contain provisions for economic incentives to foster compliance. The study concluded that UNEP has the opportunity to include economic incentives and monitoring mechanisms in its further development of international environmental law.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.003
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.015
GPT teacher head0.261
Teacher spread0.246 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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