Bibliographic record
Abstract
Though informed by theories of learning motivation in mainstream psychology, motivation research in second language acquisition has evolved somewhat independently to address the unique social, psychological, behavioral, and cultural complexities of acquiring a new communication code. Founded in the bilingual context of Canada in 1959, second language motivation research originated in a social‐psychological framework implicating attitudes and relations among linguistic communities, as crystallized in the concept of a language learner's integrative orientation towards speakers of the target language. In the 1990s, researchers began to bring this motivation research in line with cognitive theories of motivation in educational psychology, leading to a sharper focus on language learning in classroom settings. In the 21st century, the dominance of English as a global language has contributed to a conceptual reframing of language‐learning motivation in terms of self‐and‐identity goals, because learners may see themselves as aspiring members of the global community of English speakers. However, researchers also now recognize the need to pay more attention to the learning of languages other than English and to the learning of multiple languages. Another area of current thinking is the individual–contextual interactions shaping the development of language‐learning motivation, particularly drawing on complex dynamic systems perspectives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".