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Record W4220943352 · doi:10.1075/itl.21002.ady

Exploring underlying elements of the motivational self system among learners in two instructional contexts

2022· article· en· W4220943352 on OpenAlexaffabout
Fatima Ady, Eva Kartchava, Michael Rodgers

Bibliographic record

VenueITL Review of Applied Linguistics · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsCarleton University
Fundersnot available
KeywordsPsychologyIdentity (music)Value (mathematics)Exploratory researchEnglish as a second languageEnglish languageLanguage acquisitionPedagogyProcess (computing)Mathematics educationSocial psychologySociologyComputer science

Abstract

fetched live from OpenAlex

Abstract Through the lens of Dörnyei’s (2005) Second Language Motivational Self System, this exploratory study focused on two groups of Canadian newcomers learning English in the traditional classroom setting (English as a Second Language [ESL];n = 37) and the workplace (Workplace Language Training [WLT];n = 29) to determine the role of motivation in their integration into Canadian society and development of the ‘Canadian self’. The results, collected by way of a questionnaire and follow-up interviews, show newcomers holding positive attitudes towards English learning and building their Canadian identity in the process. Notably, beliefs concerning the value of employment to fulfill personal obligations and duties promoted the WLT learners’ motivation and willingness to engage with language learning significantly more than those of their ESL counterparts. Pedagogical implications are discussed.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.007
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.098
GPT teacher head0.296
Teacher spread0.198 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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