MétaCan
Menu
Back to cohort
Record W3016247221 · doi:10.1080/02188791.2020.1751063

Reproduction of nationalist and neoliberal ideologies in Nepal’s language and literacy policies

2020· article· en· W3016247221 on OpenAlexaff
Pramod K. Sah

Bibliographic record

VenueAsia Pacific Journal of Education · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIdeologyLiteracySociologyNeoliberalism (international relations)NationalismGlobalizationCurriculumLanguage planningSocial sciencePolitical sciencePedagogyLawPolitics

Abstract

fetched live from OpenAlex

The paper explores how the discourse of nationalist and neoliberal agendas have shaped the conceptions of literacy education in Nepal, the ramifications for social stratification. As the review shows, the ruling elites tactfully imposed their language, culture, and knowledge in literacy curricula in the name of national unity, but to maintain their status quo. Later, literacy planning was ideologically oriented to the neoliberalism, which overtly espoused the English language and its associated culture and knowledge as must-have literacy skills for global socioeconomic mobilities. In both cases, the local languages, culture, and knowledge have been ignored in literacy education, resulting in an ideology for minoritized groups to accept Nepali-English bilingual/bicultural literacy skills as valid and their languages, cultures, and knowledge as deficit and valueless. The article, therefore, argues that the increasing growth of globalization and neoliberal logics is altering the construct of literacy, especially in terms of its purposes and uses, taking it beyond the local cultural and communicative practices to the global.

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)0.005
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.014
Scholarly communication0.0080.007
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.275
Teacher spread0.257 · 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

Citations55
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAsia Pacific Journal of EducationSame topicSecond Language Learning and TeachingFrench-language works237,207