COVID-19 and BLM: Humanitarian Contexts Necessitating Principles from First Nations World Views in an Intercultural Social Work Curriculum
Bibliographic record
Abstract
Abstract: Unprecedented trends of complex humanitarian contexts are unfolding globally, and they are driven by numerous humanitarian crisis drivers. Two of the more recent and ongoing crisis drivers are the Coronavirus Pandemic 2019 and the Black Lives Matter (BLM) movement. While the pandemic has already caused a direct impact on unprepared health systems and caused secondary havoc on already fragile countries, the BLM movement has exposed the deeply held structural inequalities experienced by populations who do not identify as Western European. Both crisis drivers have also exposed the structural problems that have long underpinned humanitarian responses. To prepare for these complexities in humanitarian contexts, social work educators need to respond to the loud outcry for holistically educated and critically reflective social work practitioners. We argue this can be achieved through an Intercultural Social Work Curriculum informed by First Nations world views to enable a shift in student mindset from Western thought, setting the foundations for professional intercultural practice in complex humanitarian contexts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".