Positive Language Education: Combining Positive Education and Language Education
Bibliographic record
Abstract
In this paper, we discuss the notion of Positive Language Education (PLE), which stems from a combination of Positive Education and Language Education. We suggest that there are good reasons for language educators to engage in enhancing 21st century skills alongside the promotion of linguistic skills. One key set of 21st century competences that would have academic and non-academic benefits are those which promote wellbeing. Wellbeing is indeed the foundation for effective learning and a good life more generally. Drawing on ideas from Content and Integrated Language Learning and Positive Education, PLE involves integrating non-linguistic and linguistic aims in sustainable ways which do not compromise the development of either skill set, or overburden educators. We believe that there are strong foundations on which to build a framework of PLE. Firstly, many language teachers already promote many wellbeing competences, in order to facilitate language learning. There is also a growing body of research on Positive Psychology in Second Language Acquisition on which further empirical work with PLE interventions can be developed. Building on the theoretical arguments put forward in this paper, we call for an empirically validated framework of PLE, which can be implemented in diverse cultural and linguistic settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".