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Record W3035925995 · doi:10.1080/01596306.2020.1769933

Child and youth engagement: civic literacies and digital ecologies

2020· article· en· W3035925995 on OpenAlexaff
Chelsey Hauge, Jennifer Rowsell

Bibliographic record

VenueDiscourse Studies in the Cultural Politics of Education · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsBrock University
Fundersnot available
KeywordsCivic engagementCitizen journalismHopefulnessSociologyYouth engagementDigital mediaPower (physics)Public engagementMedia studiesGender studiesPolitical sciencePublic relationsPsychologySocial psychologyPolitics

Abstract

fetched live from OpenAlex

With a rise in participatory media, there has been a hopefulness about how networked media spurs on youth leadership and civic engagement, offering opportunities and power to marginalized voices and communities that are historically under-represented. As more communities began to use these networks, and as organizations and institutions began to harness digital media and creative practices for the explicit purpose of empowerment, arguments around the technological determinism and the politics of hopeful futurity began to emerge. In this special issue, international researchers foreground their interpretations of the notion of civic literacies within digital ecologies. As<br/>young people play on the terrain of global mediascapes (Appadurai, 1996), they contribute and shift them, offering us new ways to imagine our lives, creating and transforming what is possible through the contribution of their own narratives, perspectives, and stories. This is especially true in March 2020 when so many young people took to social media to share their COVID-19 anxieties in a time of uncertainty. The special issue goes some way in confronting tensions and urgencies such as racism and political uncertainties of the contemporary moment.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.062
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.107
GPT teacher head0.353
Teacher spread0.246 · 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.

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

Citations9
Published2020
Admission routes1
Has abstractyes

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