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Record W4214808446 · doi:10.15173/mjc.v13i1.2754

The inclusion of youth with lived experience of homelessness in emerging networked publics: Challenges in online storytelling and allyship

2022· article· en· W4214808446 on OpenAlexaffvenueabout
Jamie Lloyd-Smith

Bibliographic record

VenueThe McMaster Journal of Communication · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPublicsStorytellingSociologyInclusion (mineral)Public sphereArgument (complex analysis)Identity (music)Articulation (sociology)Gender studiesMedia studiesPublic relationsPolitical scienceNarrativeAesthetics

Abstract

fetched live from OpenAlex

There has been considerable debate over the extent to which vulnerable and diverse populations are politically involved in decision-making. Specifically in Canada’s homelessness sector, there is a macro-level push to include individuals with lived experience of homelessness in the policymaking process. This research paper will explore the ways in which youth with lived experience of homelessness and their allies are engaging in online forms of activism and storytelling, and ultimately examine this interaction under the idea that media is both a practice and an imaginary. This study situates this argument in the wide critique of Jürgen Habermas’ exclusionary public sphere. Instead, this paper examines the emerging concepts of “affective publics” and “networked publics” to understand how online marginalized voices are only empowered to the extent to which they perform in mediated spaces to gain visibility. By drawing on Pierre Bourdieu’s articulation of social capital, this study examines recent examples of Canadian organizations amplifying youth voices on Instagram to interrogate some of the implications that arise in online allyship and storytelling. Keywords: affective publics, networked publics, youth homelessness, lived experience, storytelling, allyship, activism, social capital

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)0.003
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 score0.026
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.105
GPT teacher head0.334
Teacher spread0.229 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

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