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Record W3111455694 · doi:10.5539/elt.v14n1p48

Close to the Heart or Close to the Home? Motivational Factors Influencing EFL Teaching as a Career Choice among Female Arab-Israeli Students

2020· article· en· W3111455694 on OpenAlexvenueno aff
Iman Alloush, Wisam Abughosh Chaleila, Abeer Watted

Bibliographic record

VenueEnglish Language Teaching · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Professional Development and Motivation
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyEnglish as a foreign languageScale (ratio)Likert scaleForeign languagePedagogyMathematics educationMedical educationDevelopmental psychology

Abstract

fetched live from OpenAlex

The purpose of this study is to examine Israeli-Arab pre-service teachers’ motivations for choosing English as a foreign language (EFL) teaching as their future profession. Data were gathered using the adapted Factors Influencing Teaching Choice (FIT-Choice) scale. Study participants (a cohort of N = 100) responded to a questionnaire of 38 motivational factors that had influenced them to choose English teaching as a future profession when entering education colleges. In addition, 20 of the participants took part in semi-structured interviews. Results revealed that the reasons Arab students become English teachers are based on a combination of intrinsic, extrinsic, and altruistic motivations. As all study participants were women, our results provide an initial indication of what draws Arab-Israeli women to the profession.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.340
Teacher spread0.306 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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