Portraits of People Who Are Homeless in the Canadian Media: Investigating the Journalists behind the Stories
Bibliographic record
Abstract
Homelessness is a complex social issue facing many communities in Canada, but most people, including policy makers have not talked or associated with someone who is homeless. Their experience is most often acquired through the skillfully framed stories of media journalists and reporters. How do journalists acquire their knowledge and frame the story in ways that impact and influence the public or policy makers in forming their perceptions? Why have they taken the position they have with the story? These and other questions about the journalists themselves, are rarely pursued, but are of value in revealing the motivation, objectivity and subjective biases in their story-telling. With social issues such as homelessness, this is of relevance as perspectives and decisions can be influenced which could have positive or negative consequences. Based on this premise, a mixed methods case study involving journalists’ motivation for writing about homelessness and how they framed their stories was pursued. Our results reveal that journalists feel that social issues, such as homelessness, are more challenging to report about as they are personally affected with what they see and hear. Their personal experiences, as well as what they want others to feel, influence what and how they frame their reporting on this situation. They do want to impact the public and policy makers – increasing their awareness and advocating for changes. Their preferences for story topics, framing considerations and impact goals reveal who they are as the people behind the media stories.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.035 | 0.017 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".