Feeling affect in a second language
Bibliographic record
Abstract
Anecdotal evidence from second language users and results from experimental studies indicate that affectively valent words are not always represented identically in a person’s first language (L1) and second language (L2) mental lexicons. The present study investigated whether such differences reflect how automatic (immediate, involuntary) the processing is of the affective element of affectively valent words, and what the relation is between this kind of processing and general word recognition efficiency for L2 words lacking affective valency. Participants were 48 L1 speakers of English with L2 French. Automaticity of processing adjectives with affective valence was operationalized using an Implicit Affect Association Task (IAAT) developed for this purpose. General efficiency in L2 word recognition was operationalized using a speeded semantic classification task with affectively neutral concrete nouns. Reaction time results from the IAAT showed that the processing of affectively valent words was less automatic in the L2 than in the L1. However, results from the semantic classification task indicated that this effect is not related to general weaker L2 word recognition abilities. Implications for an understanding of the L2 mental lexicon are discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".