Perceptions of Portuguese exporting companies on free trade agreements between the EU and third countries: The case of footwear, wine, textiles, molds and automotive sectors for Canada, UK, Mexico and Japan
Bibliographic record
Abstract
Given economic globalization and the lowering of barriers, improving export performance is one of companies' top priorities and a key determinant of effective economic growth. This research presents a literature review that addresses the concept of Free Trade Agreements (FTAs) and the different types of agreements. It also characterizes the sectors of footwear, wine, textiles, molds, and automotive. Regarding the research procedure, quantitative and qualitative data were collected from a sample of companies that export to Canada, Japan, Mexico, and the United Kingdom, belonging to the sectors of footwear, wine, textiles, molds, and automotive (N=59). Regarding the research procedure, data were collected in a quantitative study from a sample of companies that export to Canada, Japan, Mexico, and the United Kingdom within the footwear, wine, textiles, molds, and automotive sectors (N=59). The research analysis, utilizing the Chi-Square independence test, assessed the use of Free Trade Agreements (FTAs) and the motivating factors. Remarkably, "Reduction or elimination of customs tariffs" and "Improved trade opportunities" emerged as the primary motivations. The research also explored the significance of FTAs for these companies, their familiarity with them, and the challenges they faced in leveraging these agreements.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".