Bibliographic record
Abstract
Canada is often said to be sleeping next to the giant. The geographic proximity of the US creates a number of opportunities for its northern neighbour, but no doubt it severely constrains the political and economic options of a country far smaller than the US in all respects except size. On the 1 st of May 2004 a number a European countries found themselves waking up next to another giant, the European Union. How will the enlargement affect the Union’s new neighbours? What challenges does it pose? And, if need be, what can be done to mitigate the consequences of enlargement for these countries? Beyond doubt these questions will be high on the European agenda during the upcoming years. In this paper I will analyse the impact of enlargement on the EU’s (new) neighbours in particular in terms of cross-border governance. I will first focus on the unintended effects of enlargement. Next I will deal with the proactive ‘proximity policy’ which the EU has developed in order to avoid that enlargement would lead to new dividing lines in Europe. In doing so, I will raise a number of questions on the theoretical tools available to analyse this impact.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
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".