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Record W2560136014 · doi:10.5539/jel.v6n1p240

Language Curriculum Analysis of French Literature in Iranian Universities at BA

2016· article· en· W2560136014 on OpenAlexvenueno aff
Rouhollah Rahmatian, Haleh Cheraghi, Roya Letafati, Parivash Safa

Bibliographic record

VenueJournal of Education and Learning · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumBachelorChristian ministryMultidisciplinary approachMathematics educationSociologyPedagogyCurriculum mappingCurriculum developmentPsychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This article attempts to realize the dominant approach in developing the academic curriculum of language degree and French literature in Iran. It concentrates on analyzing the content of the curriculum approved by the Ministry of Science, Research and Technology in Iran and the University of Tehran. It was concluded that the first curriculum opts an approach of broad areas by considering the isolated components of language learning. It encompasses the literature of each century. Yet, Tehran University has sought to review the curriculum of Bachelor degree in 2011. With regard to the course of the general French, curriculum decisions are influenced by the corporation, specifically by French companies (under the influence of the action-oriented perspectives in language teaching). In these courses, the approach is based on the general areas. The courses in French literature are based on learning objects and are part of a multidisciplinary approach.

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)0.007
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0170.011
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.241
Teacher spread0.235 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of Education and LearningSame topicSecond Language Learning and TeachingFrench-language works237,207