Bibliographic record
Abstract
An introductionStudents of multilingualism and education at the Vrije Universiteit Brussels (VUB) have produced the next three papers, Irina Leca, Linh Nguyen Thi Thuy and Yvonne Mathole. Let us first agree on what is meant by multilingual education. It refers to the use of a second language for learning subject matter, such as advocated by the Canadian immersion programmes and the European Content and Language Integrated Learning approach, in short CLIL (see Coyle et al. 2010). The authors discuss a number of important aspects that are of interest to scholars and policymakers with an interest in education throughout the world. Two aspects can be broadly distinguished. (i) What does it mean for an individual to be multilingual with respect to his/her health in the long run (Irina Leca) and (ii) what are significant aspects of the introduction of multilingual education, i.e. the CLIL approach, in countries with little or no experience with this approach. In this case two countries are exemplified, namely Vietnam and South Africa (Nguyen Thi Thuy and Yvonne Mathole).Answers to these issues highlight general aspects of multilingualism that are not limited to Europe or the Americas but are of interest to the whole world. As Leca points out in her paper, it is well known that ageing will be one of the significant factors to be addressed in the coming decades and the relationship between a healthy brain and multilingualism is worth considering. Within the lifelong learning approach, advocated by the European Union, it is not superfluous to point out what should be well-known by now but is not, namely that early learning and lifelong practice are the keys to effective language management. Most policymakers are unfortunately unaware of this or pretend to be unaware because the adoption of these principles entails a number of far-reaching policy changes that are still controversial in many countries. Among them, learning languages in school starting from a very young age and continuing practice in secondary education and beyond.When it comes to the adoption of multilingual education many countries are still struggling with the basic principles relating to it. Among them at first sight seemingly insuperable ones, such as the shortage of teachers and lack of teaching materials. But teachers can be trained and materials can be developed. These arguments against multilingual education are often used as pretexts in order not to implement multilingual education, since they are not too difficult to overcome in time given political courage and good management.But even more important than these arguments against multilingual education are misconceptions that underlie most of the counter-arguments. One misconception is that in order to learn a language one should concentrate on that language so that interferences are excluded. Learning two languages at the same time is not something that is easy to contemplate for many non-professional linguists and/or educators with experience in language learning and teaching. This is especially the case where a prestigious language such as English is involved. Time and time again, scholars report, especially in Africa, how indigenous languages are swept aside as the medium of instruction because (i) this might hamper the learning of English and (ii) there is no use in learning an unimportant [sic] indigenous language (see Ouane and Glanz 2010; Van de Craen et al. …
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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 | 0.004 | 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 | 0.006 | 0.014 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".