How are words felt in a second language: Norms for 2,628 English words for valence and arousal by L2 speakers
Bibliographic record
Abstract
Abstract The topic of non-native language processing has been of steady interest in past decades. Yet, conclusions about the emotional responses in L2 have been highly variable. We conducted a large-scale rating study to explicitly measure how non-native readers of English respond to the valence and arousal of 2,628 English words. We investigated how the effect of a rater's L2 proficiency, length of time in Canada, and the semantic category of the word affects how L2 readers experience and rate that word. L2 speakers who had lived a longer time in Canada, and reported higher English proficiency, showed emotional responses that were more similar to those of L1 speakers of English. Additionally, valence differences between L1 and L2 raters were greater in words that L2 raters do not typically use in English. These findings highlight the importance of behavioural ecology in language learning, particularly as it applies to emotional word processing.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".