IN THE WAKE OF COVID-19: REFLECTING ON SOCIAL WORK’S CLIMATIC FUTURE
Bibliographic record
Abstract
We have moved into an era some call the Anthropocene, a time when nothing is untouched by the seeming unending expansion of modern systems (political, economic, cultural, virtual) and their inevitable global impacts. In many ways, COVID-19 intensified our awareness of this global interconnectivity not only through contact-tracing the pandemic, but also through its varied impacts on modern systems that further highlighted our ongoing dance with global and local environmental changes. The interconnected nature of our climate of change is revealing to us the partial, dualistic and ultimately limited modern worldview that continues to constrict the social justice principles our vocation of social work holds as its ideal. Something is out of balance and we need to work upon this imbalance in ways that do not deny the loss, confusion, injustices and power-grabs highlighted in the wake of COVID-19. Through approaching our climate of change in this way, we are given an opportunity to reflect on social distancing in relation to honouring boundaries, the value of slowing down modern ways of living, and the need to look more closely at our modern blocks to a sustainable future on planet Earth; in other words our climatic truth-work. In this article, I reflect on what Canadian society, the social work profession and the international community have learned (or not learned) from the pandemic about the climate of cultural change before us.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.016 | 0.036 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".