MétaCan
Menu
Back to cohort
Record W2947944890

“Ahyaanan I text in English ‘ashaan it’s ashal ”: Language Crisis or Linguistic Development? The Case of How Gulf Arabs Perceive the Future of their Language, Culture, and Identity

2011· book-chapter· en· W2947944890 on OpenAlexaboutno aff
Fatma Saïd

Bibliographic record

VenueResearch Online (Goldsmiths University of London) · 2011
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsArabicNorm (philosophy)LiteracyEveryday lifeIdentity (music)Political scienceState (computer science)Foreign languageGender studiesPedagogySociologyLinguisticsLawArtAesthetics
DOInot available

Abstract

fetched live from OpenAlex

Gulf Arabs are perceived as the most advanced of the Arabs in terms of state wealth, living standards, quality of life, education, and literacy levels for both men and women and overall opportunities availed to them by virtue of being citizens. In order for this advancement to have taken place the Gulf had to find a way of educating its people to international standards. One such way was the introduction of English in the education system at all levels and most importantly in higher education. Foreign workers from Europe, America, Canada, and Australia were brought in to assist in this moderni- zation process and the language of common communication became English. After two or three decades, there are now calls to revive Arabic and reduce the ef fect and impact of English, not only in the education system but in the everyday lives of Gulf Arabs, where the use of English in non-formal situations has become the norm. Some quarters are claiming that the Arabic language is at the beginning of its death and soon will have no speakers, if English continues to be promoted over Arabic, in the media, through domestic South Asian maids and nannies, and in the education system.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.073
GPT teacher head0.404
Teacher spread0.331 · 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 teacher head, 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
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueResearch Online (Goldsmiths University of London)Same topicMultilingual Education and PolicyFrench-language works237,207