Stronger Neighbourhood, Stronger Partnerships: A Revised European Neighbourhood Policy
Bibliographic record
Abstract
The European Commission adopted a strategic Communication on a revised European Neighbourhood Policy (ENP) in November 2015.This is the culmination of a process launched by President Juncker at the start of this Commission's mandate.The past twelve months have seen extensive work as we consulted partner countries and Member States -not just governments, but civil society, the private sector, local and regional government, social partners, International Financial Institutions (IFIs) and others.The adoption of the review is just the beginning of a process in which we will make our policy more effective and more relevant to the most urgent concerns of the EU and its partners.The ENP was originally devised to build an area of security, stability and prosperity around the EU following the enlargement of 2004.It was a period of optimism for the EU, which had succeeded in supporting the transformation of the countries of central Europe into democracies and market economies.The EU had proved its power of attraction and believed that it could now project its values and norms beyond its borders.Without offering an accession perspective, the idea was to incentivize reforms.Those partners who made the greatest advances towards democracy, human rights and rule of law would receive more: not just more funding, but also other advantages including visa facilitation and visa liberalization and more opportunities for trade.This was the basis of the so-called 'more for more' approach.With some partners, this approach has succeeded in underpinning a will to reform.In the East, we have seen the implementation of far-reaching Association Agreements and Deep and Comprehensive Free Trade Areas (DCFTAs) and progress towards visa-free short-term travel.In the South we have supported countries in their demands for 'bread, freedom and social justice', in particular stepping up our work with Tunisia as it consolidates its democracy.
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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 | 0.037 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.024 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".