Bibliographic record
Abstract
Despite fierce parliamentary opposition to the pact, including resistance from the Europe of Nations and Freedom group, whose co-chair, French presidential hopeful Marine Le Pen, condemned CETA as terrible (...) undermining thousands of jobs in Europe, the majority of members of the European Parliament (MEPs) supported CETA and the deal was passed by 408 votes to 254, with 33 abstentions. Critics of the deal have argued that the agreement will benefit only multinational companies; however, EU trade commissioner Cecilia Malmstrom said CETA would deliver economic advantages for small and medium-sized enterprises, through lower tariffs, less bureaucracy and better access to the market. The MAC is also pressing for additional trade deals with other major markets such as China and India and wants the Canadian government to invest in the necessary infrastructure to transport minerals from often remote Canadian mines to North American sea and lake ports. The EU imported EUR 61m ($64.1m*)-worth of potassic (potash-based) fertilisers from Canada in 2015, while EU exports of mineral-based chemicals to the North American country, including EUR 79.4m of nitrogenous fertilisers (largely urea) in 2015, represent a significant source of foreign exchange earnings for European companies. [...]with future trade relations between the US and Canada uncertain under the new Donald Trump-led Republican administration, which has pushed for increased protectionism in the US and demanded a renegotiation of the North American Free Trade
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.068 | 0.014 |
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".