"At risk" and "a risk": a critical discourse analysis of government and media texts exploring framings of, and responses to, people experiencing homelessness in the context of COVID-19
Bibliographic record
Abstract
This research takes place at the locus of three converging topics: the lived experience of people experiencing homelessness (PEH) in Canada, the discourses that shape and are shaped by sociopolitical framing and positioning of PEH, and the COVID-19 pandemic. The emergence of the COVID-19 pandemic in December 2019 has had a tremendous and sustained impact throughout the world. Consistent with other public health crises, COVID-19 has highlighted and exacerbated existing social inequities. With recognition that diverse experiences and identities are included among PEH, on the whole, PEH in Canada have experienced a high level of social exclusion and marginalization even prior to COVID-19. This social exclusion is both a result of, and a contributor to, dominant social discourses about PEH. In this study, I have explored ways in which PEH have been considered, framed, and responded to within the context of the COVID-19 pandemic. To do so, I used a critical discourse analysis methodology to analyze 16 government texts created in response to COVID-19, and 40 media texts addressing PEH during the context of COVID-19. The texts were selected from four urban centers in Canada: Winnipeg (Manitoba), Yellowknife (Northwest Territories), Vancouver (British Columbia) and Halifax (Nova Scotia); federal government documents were also included. Using guiding research questions informed by critical literacy theory and critical discourse analysis, the inherent subjectivity of the text (and subjective interpretations of the text); authorial choices—such as language, framings of PEH, intended audience, whose voice(s) are represented in the texts (and how); and the ways in which power is embodied in and through these texts were identified and critically examined. The findings inform a discussion of critical frames and themes that crosscut government and media discourses of PEH during COVID-19; concepts of belonging and othering in sociopolitical texts and discourses; and the ways that problems and solutions related to PEH, to homelessness, and to PEH in the context of COVID-19 have been constructed. The study concludes with a series of recommendations for authors of government/media texts, and a visual framework that can be used for critical reading and reflection.
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 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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.022 | 0.045 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".