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Record W2497925270 · doi:10.21083/surg.v8i2.3156

Effect of self-referant primes about language and memory on measures of working memory in individuals with and without second languages

2016· article· en· W2497925270 on OpenAlexaffvenue
Paul R. Marshall

Bibliographic record

VenueSURG Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsReferentPsychologyArousalSocial psychologyStereotype (UML)Cognitive psychologyPrime (order theory)LinguisticsDevelopmental psychologyMathematics

Abstract

fetched live from OpenAlex

In our multicultural North American society people whose primary language is not English may either benefit if they can fluently speak both English and another language, or they may suffer if they cannot fluently speak English. This may be partly because of stereotypes about people who can (2LS) or can’t speak a second language (W2L) which may lead to stereotype threat and arousal. Specifically, it was hypothesized that whether a prime was self-relevant would interact with the type of prime given to an individual to induce stereotype threat or arousal. To test this we subjected 2LS and W2L speakers to negative, neutral and positive primes about having a second language and then measured working memory (WM). We predicted that positive self-referent primes would enhance, whereas negative primes would disrupt WM independent of language status, and that neutral primes would not differ among language groups. Confirming this hypothesis a significant crossover interaction between prime manipulations and condition was demonstrated with no main effect of language group. These results suggest that while WM capacity does not differ between language groups when these same groups are given self-referent positive and negative primes these can induce stereotype threat and arousal which may have effects on WM through modifying physiological and psychological processes.

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.019
GPT teacher head0.316
Teacher spread0.297 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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