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Record W2036709117 · doi:10.1080/00094056.2013.815554

Social Network Sites and Young Adolescent Identity Development

2013· article· en· W2036709117 on OpenAlexaff
Geordy G. Reid, Wanda Boyer

Bibliographic record

VenueChildhood Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGlobeIdentity (music)Computer-mediated communicationPsychologyIdentity formationCitizenshipSocial identity theoryThe InternetSociologySocial psychologyPublic relationsPolitical scienceSelf-conceptSocial groupWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Social networking sites are a dominant web presence that greatly influence the formation of individual identity through digital communication and interaction. Despite caveats associated with increased usage of such online forums among young adolescents, this article sheds light on how educators can use networking sites, like Facebook, as instructional resources. It reconsiders the potential role of Facebook as a pedagogical tool that can encourage communication among teachers, students, parents, and community members to facilitate greater understanding of curricular concepts, promote ethical online behavior, and support digital citizenship of children and youth. The extensive reach of social networking sites across the globe warrants meaningful research and critical discussions about harnessing the positive impact and mitigating the detrimental effects of Facebook on identity formation among young users.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.282
Teacher spread0.266 · 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

Citations27
Published2013
Admission routes1
Has abstractyes

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