In a situation where enormous numbers learn English for international communication, what are the motivations for English mother-tongue speakers to learn other languages?
Bibliographic record
Abstract
In the UK foreign language learning at school and university is in decline. There is general agreement that there exists a widespread perception that foreign languages are neither useful nor profitable for English mother-tongue speakers. However, the European Union and the UK Government promote the idea of multilingualism and linguistic diversity in an attempt to maintain the capacity in the population. Nevertheless, one has to be motivated to expend effort for an outcome which generally is poorly rewarded. It is interesting, therefore, to know why certain adults learn or improve their competence in a foreign language when Europeans are increasingly learning English as a lingua franca. What is the motivation of such learners? The research investigates the motivation of 1,000 English mother-tongue speakers who are enthusiastic learners of various European languages. The study seeks to discriminate between the micro-level (i.e. individual learning), meso-level (i.e., educational infrastructure) and the macro-level (i.e., the influence of Europe and globalisation). The data acquired reveals the very complex reasons for this learning. Drawing from previous studies of motivation in industry, educational research and other psychological investigations, the inquiry takes a fresh look at diverse variables suggesting that certain factors and trends are evident which will be of use to language policy makers in the European Union, Canada and the United States as they attempt to maintain diversity within the foreign language curriculum. What are the reasons for language learning and are there certain profiles of language learners?.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".