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Record W2241534971

Monitoring economic partnership agreements: inputs to the negotiations and beyond

2008· article· en· W2241534971 on OpenAlexfundno aff
Michael Brüntrup, Sanoussi Bilal, Franziska Jerosch, Niels Keijzer, Christiane Loquai, Francesco Rampa, Tobias Reichert

Bibliographic record

VenueEconstor (Econstor) · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsnot available
FundersDeutsches Institut für EntwicklungspolitikBundesministerium für Wirtschaftliche Zusammenarbeit und EntwicklungInternational Fund for Agricultural DevelopmentEuropean CommissionCommission for Environmental Cooperation
KeywordsGeneral partnershipNegotiationBusinessEconomicsInternational tradePolitical scienceFinanceLaw
DOInot available

Abstract

fetched live from OpenAlex

"The upcoming Economic Partnership Agreements (EPAs) between the European Union and African, Caribbean and Pacific (ACP) countries and regions are extremely challenging. Conceived as a follow-up to the non-reciprocal trade preferences granted to the ACP since 1975, the aim of the EPAs is sustainable development and poverty reduction through the establishment of a Free Trade Area. Many challenges and opportunities arise from this ambitious trade and development partnership between some of the world's poorest and richest countries. Throughout the negotiations, several stakeholders have expressed concerns about the possible negative effects in ACP countries that risk jeopardizing the developmental impacts of EPAs. The ambition, as well as the uncertainties around EPAs make a results-oriented monitoring of the agreements imperative. This study, commissioned by the German Ministry for Economic Cooperation and Development, addresses the possible goals of EPA monitoring, drawing conclusions on what broad areas need to be monitored, which principles should be followed and which stakeholders involved. The study then outlines the challenges involved in implementing a results-oriented monitoring system: which steps need to be taken, how best to derive indicators, which characteristics the latter should have and how they may be identified. The study also presents a number of recommendations on how to ensure that monitoring is given due consideration in the EPA legal texts." (author's abstract)

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.031
GPT teacher head0.285
Teacher spread0.254 · 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 teacher head, not a consensus.

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

Citations3
Published2008
Admission routes1
Has abstractyes

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