Positive and negative emotions underlie motivation for L2 learning
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 role of basic emotions in SLA has been underestimated in both research and pedagogy. The present article examines 10 positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love) and 9 negative emotions (anger, contempt, disgust, embarrassment, guilt, hate, sadness, feeling scared, and being stressed). The emotions are correlated with core variables chosen from three well-known models of L2 motivation: Gardner’s integrative motive, Clément’s social-contextual model, and Dörnyei’s L2 self system. Respondents came from Italian secondary schools, and most participants were from monolingual Italian speaking homes. They described their motivation and emotion with respect to learning German in a region of Italy (South Tyrol) that features high levels of contact between Italians and Germans. Results show that positive emotions are consistently and strongly correlated with motivation-related variables. Correlations involving negative emotions are weaker and less consistently implicated in motivation. The positivity ratio, that is, the relative prevalence of positive over negative emotion, showed strong correlations with all of the motivation constructs. Regression analysis supports the conclusion that a variety of emotions, not just one or two key ones, are implicated in L2 motivation processes in this high-contact context.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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