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Record W2803116584 · doi:10.5539/jel.v7n4p136

“L2 Motivational Self System” and Learning German in Iran

2018· article· en· W2803116584 on OpenAlexvenueno aff
N Haghani, Mostafa Maleki

Bibliographic record

VenueJournal of Education and Learning · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistic Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsGermanPsychologyContext (archaeology)Foreign languageIdeal (ethics)Language acquisitionMathematics educationSubject (documents)LinguisticsEpistemologyComputer science

Abstract

fetched live from OpenAlex

Examining the reasons for the increasing number of Iranians learning German and creating of a first theoretical basis for that is the subject of this paper. In this regard, 370 Iranian learners of German from the German Language Institute in Tehran were questioned and their motivations were studied mainly based on the theory of “L2 Motivational Self System” (L2MSS). Investigating this research related to the psychological concept of “possible selves” and comparing it with the results of research conducted in the learning of English indicates that the motivation for learning German has a significant relationship with the components of the L2MSS, namely, L2 Ideal Self, L2 Ought-to Self, and L2 Learning Experiences. The achievement of this research can be effective in adopting foreign language policies in formal and informal educational areas in Iranian learning context.

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)0.002
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.040
GPT teacher head0.325
Teacher spread0.285 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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