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Record W2804743930 · doi:10.5539/ijel.v8n5p135

Language Policy and Its Consequences on Sindhi Language Teaching in Sindh, Pakistan

2018· article· en· W2804743930 on OpenAlexvenueno aff
Habibullah Pathan, Syed M. Zafi S. Shah, Shoukat Ali Lohar, Ali Raza Khoso, Sadia Memon

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLanguage policyUrduEliteFirst languagePoliticsIndigenousLiteracyPolitical scienceIndigenous languageNational languageMultilingualismLinguisticsSociologyPedagogyLaw

Abstract

fetched live from OpenAlex

This paper examines the language teaching policy in Pakistan and its consequences on Sindhi language in Sindh province. The paper argues that such language policy has attempted to marginalize other local and indigenous languages of the country in general and Sindhi language in particular. Politics is actively engaged in determining the status of languages in the country. English and Urdu being the languages of the dominant social group, that is, the ruling elite in the country enjoy status of official and national languages respectively whereas languages of the marginalized group are excluded from the domain of education, literacy and power. The paper, thus, draws attention of the language policy makers to linguistic human rights and argues that all the languages should be treated equally. Education being inborn right of human being should be acquired in one’s own mother tongue; this is the only solution to cope with present and future challenges in Pakistani educational 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 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)0.003
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 score0.104
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.037
GPT teacher head0.476
Teacher spread0.439 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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Same venueInternational Journal of English LinguisticsSame topicMultilingual Education and PolicyFrench-language works237,207