The inclusion of youth with lived experience of homelessness in emerging networked publics: Challenges in online storytelling and allyship
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".