COVID-19 pandemic, climate change and Indigenous knowledges informing the future of social work
Bibliographic record
Abstract
The COVID-19 pandemic revealed more fully social, economic, racial, and environmental disparities and offers a glimpse into the future. We recognize this moment as an opportunity to not only address the current pandemic, but also the climate crisis, which promises even more intense disruptions and disasters. This is not new for Indigenous people who have already experienced the end of their worlds through colonization. Indigenous people have adapted to change by relying on the knowledge of their lands which ensured their survival and will help them to prepare for climate change. Social work must seize this moment to address conditions by focusing on environmental and Indigenous ways of knowing to remain relevant as a positive force for social change. We identify four places to seek transformation in the 21st century: social work practice by moving towards anticolonial practice, the capitalist economy by moving to degrowth, hierarchical social welfare by promoting mutual aid, and the industrial food system by moving to food sovereignty. Through an exemplary case study, we illustrate how these approaches incorporate Indigenous knowledges and translate to social work practice. We explore the roles that social work can use to create a future that ensures justice, prevents harm and promotes a thriving world.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.046 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".