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Record W3080872415 · doi:10.1017/s1366728920000474

How are words felt in a second language: Norms for 2,628 English words for valence and arousal by L2 speakers

2020· article· en· W3080872415 on OpenAlex
Constance Imbault, Debra Titone, Amy Beth Warriner, Victor Kuperman

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBilingualism Language and Cognition · 2020
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsMcGill UniversityUniversity of WaterlooMcMaster University
Fundersnot available
KeywordsPsychologyValence (chemistry)Emotional valenceArousalLinguisticsSecond languageFirst languageCognitive psychologySocial psychologyCognition

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.281
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it