Bibliographic record
Abstract
The European Union (EU) is founded on the principle of ‘unity in diversity’, that is the diversity of cultures, customs and beliefs, and languages. Today around 445 million people, who together speak over 80 languages, find their home in the 27 member states of the EU. As the EU greatly values its rich cultural and linguistic diversity, it is committed to safeguarding its 24 official languages and promoting the learning of multiple languages in the Member States of the EU. One of the main goals of the EU’s language policies and initiatives is for every citizen to be able to speak two languages in addition to their mother tongue. This goal, first formulated in 1995, is also known as the ‘mother tongue + 2’ formula. In the 2002 Barcelona European Council, the EU called for the improvement of education in order to give students the chance to develop language skills in two foreign languages in school. In reality, however, not all citizens are convinced of the merits of speaking various languages, which shows that linguistic diversity is not yet the norm. Besides that, publications of the European Commission show that only a quarter of EU citizens are able to hold a conversation in two foreign languages. As Member States of the EU have the right to decide on their own language policy due to the principle of subsidiarity, the influence of the EU’s language policy is limited. For this reason, the aim of this thesis is to investigate the reality of compliance to the ‘mother tongue + 2’ formula through the analysation of national language policies and language learning in two Member States, namely the Netherlands and Hungary. By conducting qualitative literature review this thesis found that in both countries the education system plays an important role in the acquisition of foreign language knowledge. However, in both countries a lot of improvements can be made in order to assure that every citizen learns two languages besides their mother tongue.
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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 | 0.006 |
| 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 | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.036 |
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".