‘We’re handing out freezies’: Climate-related professional challenge and changing social work practices in the homelessness sector
Bibliographic record
Abstract
Abstract Climate change’s disproportionate impact on people experiencing homelessness has a collateral effect on community-based organizations. Relying on the concept of ‘professional challenge’, this research is interested in: (1) the ways that social work professionals are challenged by new climate realities; (2) the tensions they navigate when their actual work differs from anticipated and prescribed work; and (3) the strategies they put in place to alleviate these tensions. We facilitated three focus groups with seventeen social work professionals from various community-based organizations working in homelessness in Quebec (Canada). A thematic and argumentative analysis shows that professionals rely on three strategies to overcome the ‘climate challenge’: the quantitative increase in services, the adaptation of practices, and the confrontation of obstacles. They illustrate how new climate realities widen the gap between anticipated/prescribed work and actual work and show that in the face of emergencies, actual work is conducted within a logic of adaptation, rather than in a transformative paradigm. Additionally, results show that the gap between anticipated/prescribed work and actual work generates teleological, ethical, and professional identity tension. To alleviate these tensions, professionals engage in emotional and discursive labor by constantly (re)negotiating guiding axiological, humanitarian, and ethical principles. Implications for practice are discussed.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.033 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".