Learning Green Social Work in Global Disaster Contexts: A Case Study Approach
Bibliographic record
Abstract
Green social work (GSW) is a nascent framework within the social work field that provides insights regarding social workers’ engagement in disaster settings. Although this framework has recently garnered more attention, it remains under-researched and underdeveloped within the context of social work research, education, and practice in Canada and internationally. To further develop GSW in social work education and professional training, we considered how social work students and practitioners can use a learning framework to understand the impact and build their capacities to serve vulnerable and marginalized populations in diverse disaster settings. To do this, we developed a four-step case study approach, as follows: (1) provide detailed background information on the cases, (2) describe how each case is relevant to social work, (3) discuss how each case informs social work practice from a GSW perspective, and (4) provide recommendations for social work practitioners and students using GSW in future disaster-specific efforts. This case study approach centers on natural, technological, and intentional/willful hazards that examine current GSW research–practice engagement in Canada and internationally. Applying this four-step case study approach to three extreme events in Canada and internationally (a natural hazard, a technological hazard, and an intentional/willful hazard) illustrates it as a potential method for social work students and professionals to build their GSW capacities. This will assist in building the resilience of Canadian and international communities—especially those who have been historically marginalized. This article sheds light on how current social work education and professional training should develop new approaches to incorporate the GSW framework into the social work curriculum at large in order to prepare for future extreme events while incorporating environmental and social justice into research and practice.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".