Commentary: Motivation for Learning Languages Other Than English in an English‐Dominant World
Bibliographic record
Abstract
The majority of recent research on language learning motivation has reportedly focused on English as a target language, typically in relatively homogeneous, secondary and postsecondary ‘foreign language’ settings. How applicable, then, are the theories and findings undergirding that research to our understanding of the contemporary challenges and processes involved in the learning of languages other than English (LOTEs) – whether by non‐Anglophones choosing additional or alternative languages, or for Anglophones choosing to learn a different language? And how is motivation theory itself evolving in light of the emerging role of English as a global language and a greater emphasis on sociopolitical, sociocultural, economic, and ideological aspects of language learning in diverse contexts, on the one hand, and a concomitant de‐emphasis of deficit‐oriented notions of learners’ shortcomings or traits in acquiring or using another language, on the other? What research methods are being used? Finally, how is current motivation research taking into account multilingual experiences (i.e., involving three or more languages), rather than just the learning of one additional (foreign) language? In this commentary piece, I address questions such as these by drawing on insights from the nine articles and other related sources and also offer some of my own perspectives drawing from research on Chinese and other languages.
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.047 | 0.050 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".