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Record W2955225541 · doi:10.5539/ijel.v9n4p252

Power, Ideology and Identity in Digital Literacy: A Sociolinguistic Study

2019· article· en· W2955225541 on OpenAlexvenueno aff
Fatima Zafar Baig, Wajeeha Yousaf, Fareeha Aazam, Sarah Shamshad, Iqra Fida, Muhammad Zammad Aslam

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologySociologyLiteracyIdentity (music)Power (physics)Social mediaDimension (graph theory)LinguisticsMedia studiesSocial psychologyGender studiesEpistemologyPsychologyAestheticsPolitical sciencePedagogyLaw

Abstract

fetched live from OpenAlex

This study investigates the significance of digital media in terms of social implications. It draws its theoretical insights from the Darvin and Norton model of investment (2015) as it gives purely a new dimension to the concept of digital literacy. The study is designed in order to evaluate some important aspects of Social media, particularly Facebook, as an important digital literacy practice. Firstly, the study examines the way power is operated in the digital mediated construction of social identities. Certain social identities position other identities and accord or refuse them power. These even shape social ideologies and identities as English-language speakers hold a privileged position in society while Urdu-language speakers are marginalized all over the world. Secondly, it explores the role of digital media in the investment of language and digital literacy practices to represent social ideologies at three different angles of marriage, adulthood and family. Having established a sampling frame consisting of nine Facebook pictorial postings from three Facebook pages, the findings suggested that the text and visual representations of Facebook postings use various linguistic features like literary devices that are playing an evident role in the representation of social ideologies.

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)0.001
metaresearch head score (Gemma)0.177
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.177
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.371
Teacher spread0.358 · 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 designObservational
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

Citations21
Published2019
Admission routes1
Has abstractyes

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