Commentary: Motivation for Learning Languages Other Than English in an English‐Dominant World
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it