Cross-border trade in services under modern EU trade agreements : An example: Boosting Finnish trade in services
Bibliographic record
Abstract
International trade has taken centre stage in the international politics, with even trade wars making headlines. The World Trade Organization (WTO) is failing due to the suspension of the Doha-round negotiations and its Appellate Body being rendered inoperative. This has meant that the focus of trade negotiations has shifted more towards the bi- and plurilateral level discussions. These new generation trade agreements include the liberalisation of services, a sector which has been one of the latest and perhaps most difficult fields to liberalise. This study takes the Finnish services trade under the Comprehensive Trade and Economic Agreement (CETA) as an example case in point. As far as trade agreements go, CETA, the mega-regional trade agreement between the EU and Canada has been a trail blazer: the first one to come into force and start to take effect. It extensively liberalises trade in services. \n \nThe aim of this study is to find out how new generation EU trade agreements can promote Finnish trade in services. The theory section reviews literature on trade policy and trade in services and draws together a framework useful for the purpose of this study. The applied research approach was a qualitative one, since the researcher wanted to understand an ongoing phenomenon more deeply. Both primary expert interviews and secondary textual data were used as source of information for a qualitative thematic analysis. \n \nThe main results are focused on how the agreements can be foreseen to benefit companies. The potential for new trade in services that EU trade agreements grant for European (and Finnish) companies is significant and the Canadian market can serve as a pilot for other destinations. The economic impact on Finnish companies’ service exports would seem to be limited as Canada is not among Finland’s biggest trade partners. That could change, however, as the countries have a lot in common. The responsibility for taking advantage of these freshly negotiated comprehensive preferential trade agreements (PTAs) lies in the hands of companies, as the benefits of preferential trade agreements do not take effect automatically. The key in being able to exploit the new opportunities is providing the right advice and easily available support and guidance for companies. For that, cooperation on the EU and national levels are needed, as well as pooling the knowledge of the public and the private sector.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".