Bibliographic record
Abstract
Even though there were linkages between European Union trade policy and “values” before 2009 (in particular with the institution of dialogues under the various association agreements concluded before 2009 or via the link between development policy and trade), linking non-trade policy objectives (NTPOs) and EU values to EU trade policy has become an increasingly prominent feature. The further politization of trade policy with greater power of the Parliament, which must give its consent to the conclusion of trade agreements since the Lisbon Treaty (Article 207 Treaty on the Functioning of the European Union, TFEU), and the rising citizen concerns on the impact of some trade agreements on NTPOs – as shown by the manifestations related to the Transatlantic Trade and Investment Partnership (TTIP) and EU–Canada Comprehensive Economic and Trade Agreement (CETA) – has created more political pressure to enhance these linkages. Finally, the realization that trade can be a useful tool for achieving NTPO objectives, for example in promoting policies in line with climate change objectives, has also called for the multiplication of these trade/NTPOs linkages in bilateral, regional and multilateral engagement with third countries (using soft power via dialogues and non-binding agreements) but also for the enhancement and proper enforcement of NTPO provisions in trade agreements (moving toward a stronger hard law approach in the future). The latter is notably the main objective of the most recent European Commission Communication published in June 2022 on the power of trade partnership as tools to foster green and just economic growth.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.128 | 0.048 |
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".