Monitoring economic partnership agreements: inputs to the negotiations and beyond
Bibliographic record
Abstract
"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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.085 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.029 | 0.035 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".