Bibliographic record
Abstract
Between 2018 and 2023, Portugal ranked among the top ten EU countries sending students to the UK. However, the number of inbound Portuguese students decreased by nearly 30% in 2022/23, while the number of Portuguese students enrolled in UK TNE programmes has steadily increased, reaching 620 students in 2022/23. Although the UK and Portugal have a long history of cooperation in science and higher education, TNE collaborations have only emerged in recent years, likely spurred by the COVID-19 pandemic and the repercussions of Brexit. Master’s students make up the largest group of TNE students, followed by undergraduates. Social sciences, humanities, and the arts are the most popular subjects in current UK-Portugal TNE programmes. Most TNE students are enrolled in distance learning or dual award programmes (with a physical mobility period). These partnership models, governed by complex regulatory frameworks, encompass various structures and significantly influence the future scope and nature of UK-Portugal TNE collaborations. Additionally, the presence of international students and providers in Portugal adds further complexity to bilateral UK-Portugal TNE partnerships. In this context, the British Council plays a pivotal role in facilitating dialogue, providing intelligence and consultancy, fostering partnerships between the two countries. Finally, this report analyses the opportunities and challenges inherent in the Portugal-UK higher education relationship, focusing on TNE in the current context using a SWOT framework (Strengths, Weaknesses, Opportunities and Threats). The practical implications of the SWOT analysis can be significant for the decision-making process at the institutional level.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.008 |
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".