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Record W2765166186 · doi:10.1504/ijsmile.2017.10008675

The adolescent bricoleur: constructing identities through social networking sites

2017· article· en· W2765166186 on OpenAlexaff
Laura Morrison, Anne Burke, Janette Hughes

Bibliographic record

VenueInternational Journal of Social Media and Interactive Learning Environments · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsMemorial University of NewfoundlandOntario Tech University
Fundersnot available
KeywordsAffordanceIdentity (music)SociologyOnline identityUploadQualitative researchPsychologyPedagogyComputer scienceWorld Wide WebSocial scienceThe Internet

Abstract

fetched live from OpenAlex

Adolescents are at a stage in life where their sense-of-self and identity are evolving. With increasing access to ever-more advanced technologies, it is important to explore the implications for the adolescents who use them. This research aimed to examine the construction, deconstruction and reconstruction of adolescent identities through an exploration of the design choices and social practices of elementary students on two social networking sites. Using a mixed-method research approach of qualitative case study analysis and quantitative surveying, we investigated the relationship between a multiliteracies pedagogy and the development of adolescent digital literacies and identity. Findings from the research indicate that social networking sites provide youth a platform in which to explore their identity. With such features as status updates, video/photo uploads, discussion threads and the 'like' and comments functions, these sites facilitated social interaction and identity performance amongst the students during class time and after-school. To maximise the academic and social affordances, however, it is necessary to build in lessons and/or scaffolding to encourage thoughtful and genuine online interaction.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.029
GPT teacher head0.319
Teacher spread0.290 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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