Critical Discourse Analysis of Indigenous Homelessness: On Social Problems and Silences in Alberta News Media
Bibliographic record
Abstract
Following the Covid-19 pandemic, homelessness became much more visible and dire social crisis within Calgary and Edmonton. Despite comprising a fraction of Calgary and Edmonton’s overall population, Indigenous peoples disproportionately represent the homeless population but are rarely discussed by the news media. This Indigenous media deficiency is sharpened by the lack of qualitative research that studies the communication of Indigenous homelessness in the news media. Drawing on Van Dijk’s critical discourse, this study employs a critical discourse analysis to ask, “how is Indigenous homelessness discussed as a social problem in Alberta news media?”. This research constitutes the first qualitative study on the discourse of Indigenous homelessness in news media. Findings identified the dominant themes of homelessness to be: accidentally becoming homeless, homeless individuals as welfare freeloaders, violence, danger, social disorder, the criminalization of homelessness, drug addicts and alcoholics. Together, findings suggest that these dominant themes operate to blame individuals, remove responsibility from the system, create public resentment, construct public fear, and dehumanize homeless people through situational links to poverty, disorder, disease, and violence. This study argues that homeless people undergo a process of othering, leaving them primarily spoken for by journalists in the news media. This study offers insight into Indigenous themes of homelessness, including the overrepresentation of homeless statistics, the cycle of homelessness and reconciliation. However, main findings identify the operation of a Western discourse, where the ideology of individualism and the cultural values of hard work, wealth, property, and self-sufficiency silences the settler-colonial legacies attributable to Indigenous homelessness. Alberta’s news media discussions of homelessness disenchant the unique oppressions Indigenous peoples face which increase their vulnerability to becoming homeless. Conclusively, this analysis reveals the important of studying the communication of social problems through an Indigenous lens to deconstruct hegemonic portrayals and reinstate the voices of our most vulnerable.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".