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Record W2955848678 · doi:10.5539/ijel.v9n4p288

Literacy, Identity and Gender: A Case Study of Love Letter Writing Practices from Pakistan

2019· article· en· W2955848678 on OpenAlexvenueno aff
Fatima Zafar Baig, Naveed Ahmed

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologySituatedLiteracyPerspective (graphical)Identity (music)Meaning (existential)SociologyGender studiesSocial practiceCultural identityQualitative researchSocial psychologyPsychologySocial scienceAestheticsPedagogyPolitical sciencePoliticsLawHistory

Abstract

fetched live from OpenAlex

This study aims to explore the identity (ies) and ideology (ies) embedded in the love letter writing practices of the people particularly in Pakistan. It brings forth the established social and cultural practices and thoughts of the letter writers from gender perspective. This study investigates the ways in which gender identity is enacted within the language of love letters. Being a significant social literacy practice, the writing of a letter is rooted in a particular social situation. Like all other types of literacy objects and events, the activity advances its meaning and significance from being situated and positioned in cultural beliefs, values, and practices. A case study of the young Pakistani couple has been conducted in this regard where the language used by couple in their love letters is analyzed from ideological perspective. The data are comprised of the love letters which are analyzed qualitatively. Street’s Ideological Model has been employed for qualitative analysis. The research finds out that the ways in which people communicate are constrained by the structures and forces of those social institutions within which they live and function. It also highlights the substantial role of language in the constructions and representations of social and cultural beliefs and values.

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)0.000
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.374
Teacher spread0.336 · 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.

Study designNot applicable
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

Citations1
Published2019
Admission routes1
Has abstractyes

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