Adaptability, Interdisciplinarity, Engageability: Critical Reflections on Green Social Work Teaching and Training
Bibliographic record
Abstract
The upward tendencies of global climate change, disasters, and other diverse crises have been urgently calling for green social work (GSW) interventions which engage a holistic approach to explore diverse societal dimensions' compounded influences on inhabitants' individual and collective health and well-being in disaster settings. Though globally gaining more attention, GSW has been slow to develop in the Canadian social work curriculum and professional training. This deficit jeopardizes integrating environmental and climate justice and sustainability in social work research and practice in Canada. In response to this pedagogical inadequacy, this article employs a critical reflection approach to examine two authors' two-academic-year teaching-learning and supervision-training experiences of GSW-specific in-class and field education in a Master of Social Work program. The content analysis illustrates three essential components for GSW-specific teaching and training, namely adaptability, interdisciplinarity, and engageability. These components enhance the prospective social workers' micro-, mezzo-, and macro-level practices to better support individuals, families, and communities affected by extreme events and promote their health and well-being in disaster and non-disaster scenarios. These GSW-specific pedagogies shed light on the fact that integrading climate change, disasters, and diverse crises in pedagogical innovations should be encouraged beyond the social work profession. A multidisciplinary multi-stakeholder engagement approach would comprehensively investigate and evaluate the essential components and evidence-based strategies that better serve inhabitants and promote resilience and sustainability.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.022 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".