Media Representations of Homelessness in Three Mid-Sized Canadian Cities: Integrative Review and Comparative Analysis
Bibliographic record
Abstract
The media play a pivotal role in framing narratives about complex social issues like homelessness. This study conducts a comparative analysis to evaluate how homelessness is portrayed within print and social media in three Canadian mid-sized cities. We explore similarities and differences between news print media and Facebook conversations on the broad topic of homelessness with respect to temporal distributions of discussions about homelessness, conversations about encampments, the diversity of voices included in social and print media reports, and the use of stereotypical and stigmatizing discourse about homelessness. Our study reveals seasonal variations in discussions about homelessness within traditional media, while social media maintains a more consistent conversation throughout the year. In both media types, however, the discussions about encampments are frequently intertwined with themes of crime and addiction. Moreover, we observe greater inclusion of voices from individuals with lived experience in print media. We find that discussions within both platforms echo common myths associated with homelessness, perpetuating negative conceptions in the broader community. In light of our findings, we advocate for greater editorial oversight on how discussions about homelessness are framed in print media, technological advancements to control negative conversations on social media platforms and encourage the inclusion of voices from those with lived experience. Our research contributes to an underexplored area, as we illuminate the mechanisms of discourse that shape public perceptions and attitudes about homelessness in both print and social media.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.042 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".