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Record W2067448863 · doi:10.5539/elt.v5n8p31

Higher Education Institutions without Foreign Language Continuity

2012· article· en· W2067448863 on OpenAlexvenueno aff
Slavica Čepon

Bibliographic record

VenueEnglish Language Teaching · 2012
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsForeign languageGrammarObstacleAttritionSecond-language attritionLinguisticsHigher educationLanguage assessmentPsychologyMathematics educationLanguage educationPedagogyComprehension approachPolitical scienceMedicine

Abstract

fetched live from OpenAlex

This article attempts to fill a research gap in the literature where there is no research in the available literature on the general foreign language/foreign-language-for-specific-purposes discontinuity at the secondary/tertiary interface. The article reports on the findings of a study that aimed to acquire the opinions of the teachers of foreign languages for specific purposes regarding various approaches to managing a general foreign language/foreign-language-for-specific-purposes transition period. Namely, at some European, including Slovenian, institutions of higher education there is no foreign language instruction for first-year students. As a consequence, discontinuity at this crucial secondary/tertiary interface may give rise to general foreign language attrition - an obstacle to subsequent foreign-language-for-specific-purposes learning in higher education. The results of in-depth interviews with 29 teachers point towards grammar in the second year as a factor interconnecting interrupted general foreign language instruction with foreign languages for specific purposes.

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)0.003
metaresearch head score (Gemma)0.011
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 score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0050.003
Open science0.0010.008
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.023
GPT teacher head0.350
Teacher spread0.327 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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