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Integrative Motivation: Changes During a Year‐Long Intermediate‐Level Language Course

2004· article· en· W2109870329 on OpenAlexaff
R. C. Gardner, Anne‐Marie Masgoret, Jeff Tennant, Ljiljana Mihić

Bibliographic record

VenueLanguage Learning · 2004
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyLanguage acquisitionAnxietySecond-language acquisitionDevelopmental psychologyCognitive psychologySocial psychologyLinguisticsMathematics education

Abstract

fetched live from OpenAlex

The socioeducational model of second Language acquisition postulates that Language learning is a dynamic process in which affective variable influence Language achievement and achievement and experiences in Language learning can influences some affective variables. Five classes of variable are emphasized: integrativeness, attitudes toward the learning situation, motivation, Language anxiety, and instrumental orientation. The present study of a 1‐year intermediate‐level French course reveals that some affective characteristics are more amenable to change than others, and that patterns of change over time are moderated by achievement in the course. Related findings demonstrate very few differences on the affective measures from one class section to another, and that day‐to‐day levels of state motivation are largely invariant, whereas state anxiety might be influenced by environmental events.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.259
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), 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

Citations322
Published2004
Admission routes1
Has abstractyes

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