Emotions that facilitate language learning: The positive-broadening power of the imagination
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 imagination is powerful, in part, because of the emotions that can be activated by imagining future states. Imagined future states are a key feature of the L2 self-system proposed by Dƅrnyei, and emotion may be the key to the motivational quality of the imagined future self. In particular, this paper focuses on positive anticipated and anticipatory emotions related to language learning. It is argued that, in general, positive emotion has a different function from negative emotion; they are not opposite ends of the same spectrum. Based on the work of Fredrickson, we argue that positive emotion facilitates the building of resources because positive emotion tends to broaden a person’s perspective, opening the individual to absorb the language. In contrast, negative emotion produces the opposite tendency, a narrowing of focus and a restriction of the range of potential language input. This article draws a framework for finding a balance between the positivebroadening and negative-narrowing emotions in the language classroom, and beyond. The emotion system is an engine for the positive-broadening power of the imagination.
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 | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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