International perspectives on trends in languages learning in the 2020s: part one: profiling the UK and the Republic of Ireland
Bibliographic record
Abstract
Nations around the globe are reconsidering approaches to languages education, in cycles of remarkable similarity and focused on common as well as particular aspects of their contextual landscapes. This paper, one of a series of profile and comparison studies, draws on the experiences of the author working on projects in Australia, conducting comparative investigations of languages education policies in the UK and The Republic of Ireland, as part of broader research on languages education policy and practices in nations, countries and regions from around the globe, including Finland, Scotland, Ireland, England, Wales, Canada, the US, Germany, Bhutan, Nauru, New Zealand and Singapore, As Australia moves towards developing a National Plan and Strategy for Languages Education, and national projects investigate benefits of an early start and profiling case studies of successful practice, there are valuable insights and lessons to be learned from comparisons with others' approaches- both like and less like others- and from comparison of themes particular to current contextual circumstances, and prospectively.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".