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Record W2913909090 · doi:10.1111/lit.12180

Adolescents' Agentic Work on Developing Personal Pedagogies on Social Media

2019· article· en· W2913909090 on OpenAlexaff
Lynde Tan, Beaumie Kim

Bibliographic record

VenueLiteracy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversity of Calgary
FundersNational Institute of Education
KeywordsAgency (philosophy)Participatory cultureSocial mediaMedia literacyLiteracySociologyCitizen journalismPedagogyFormal learningInformal learningPsychologyDigital mediaDigital literacyMedia studiesSocial sciencePolitical science

Abstract

fetched live from OpenAlex

Abstract While current research points out that young people are developing emerging culture of learning in informal spaces, less is known about such digital literacy practices in the Asian contexts where the notion of literacy tends to refer to school literacy. Research on young people's online participatory culture continues to suggest that social media offer affinity spaces where extensive knowledge is acquired, constructed and produced outside of schools. In this paper, we use two case studies on social media as illustrative examples to understand how adolescents shape their learning online. We aim to contribute to the ongoing dialectics on social media and learning by examining how adolescents exhibit agency online. We argue that social media such as Facebook offer high learner agency environments for adolescents to participate in self‐initiated enterprise and allow them to develop personal trajectories for learning. The case studies presented in this paper suggested that the adolescents' pursuit of their passions on online affinity spaces gave rise to intellectual friendships and the development of personal pedagogies.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0040.006
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.002
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 head0.069
GPT teacher head0.371
Teacher spread0.302 · 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 designQualitative
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

Citations3
Published2019
Admission routes1
Has abstractyes

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