Giving voice to the street: a case study of Toronto’s Street Voices magazine
Bibliographic record
Abstract
This Major Research Paper investigates Street Voices Magazine as an instrument and communications tool to engage and empower street youth in Toronto. The following questions guided my study: What are the ways in which Street Voices Magazine gives voice to the marginalized and silenced? Why is Street Voices Magazine an appropriate medium for connecting with street youth? A mixed-method approach was used to analyze the texts and images in three issues of the magazine to determine the effectiveness of the print medium, what these texts and images suggest about the motivations of the contributors, and whether the magazine meets its objective of serving street youth. The study suggests that the transformative potential of the arts, the role of the magazine in fostering in the contributors the identity of an artist, and the lack of other spaces for expression are significant themes that underpin Street Voices Magazine’s appeal and effectiveness. The study also leads to suggestions for further research, which could improve an understanding of this diverse demographic and confirm the impact of Street Voices Magazine.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".