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Record W4390401512 · doi:10.23860/jmle-2023-15-3-2

Everyday engagement with mobile phones in an urban slum in Delhi

2023· article· en· W4390401512 on OpenAlexaff
Simranjeet Kaur, Sunita Singh

Bibliographic record

VenueJournal of Media Literacy Education · 2023
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsBrock University
FundersTata Trusts
KeywordsSlumMobile phoneEthnographyParticipant observationLiteracyEveryday lifeSociologyMedia literacyPhoneQualitative researchPoint (geometry)PsychologyPublic relationsInternet privacyPedagogyPolitical scienceSocial scienceComputer science

Abstract

fetched live from OpenAlex

This ethnographic case study presents findings of an 18-month research study focusing on the ways in which families residing in an urban slum were using mobile phones and how this use supported literacy practices. Data collection included participant observations and interviews with 42 participants including parents, children and community members. Results of the data analysis indicated that in this urban slum, most participants owned a mobile phone which provided multiple entry points to learning. The phones ushered in new ways of brokering knowledge where children acted as ‘experts’ and enabled parents to perform everyday tasks while parents mediated as cultural brokers and fostered religious and cultural practices and knowledge of the mother tongue. The implications of the study point to the evolving nature of literacy practices, the versatility of the device, the uneven landscape of smartphone use and the limitations posed by the schooling contexts.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.314
Teacher spread0.292 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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