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Record W2300454348

Regional Institutional Approaches to Environmental Displacement and Climate Change in Developing Countries: The Role of Law and Policies in the East African Community (EAC)

2010· article· en· W2300454348 on OpenAlexaff
Irene Connie Tumwebaze

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsYork University
Fundersnot available
KeywordsPolitical scienceClimate changeCorporate governanceEnvironmental governanceDisplacement (psychology)Environmental lawRegional scienceEnvironmental planningDevelopment economicsEconomic growthEnvironmental resource managementGeographyBusinessEconomicsLawEcology
DOInot available

Abstract

fetched live from OpenAlex

The paper examines current efforts and debates on regional and global institutional approaches to environmental displacement. The focus of this paper is particularly on how effective are regional institutional approaches. Regional initiatives promoting environment and climate change governance have complemented the establishment of global principles, and continue to be significant in examining and implementing those principles. The paper explores various criteria for choosing between regional and global institutional approaches and discusses an analytical framework for responding to environmental challenges regionally. The paper uses the East African Community (EAC) as a regional bloc and examines how it may respond to the problem of environmental displacement and climate change in the region. An effective architecture for tackling the problem is feasible but this will require re-thinking current partnerships, cooperation and collaboration amongst the EAC member states.

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)0.010
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.029
Scholarly communication0.0080.005
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.120
GPT teacher head0.291
Teacher spread0.171 · 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
Published2010
Admission routes1
Has abstractyes

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Same venueSSRN Electronic JournalSame topicClimate Change, Adaptation, MigrationFrench-language works237,207