Welcome to the first issue of Transformative Social Work: A special issue on the impacts of the COVID-19 pandemic
Bibliographic record
Abstract
Welcome to the inaugural issue of Transformative Social Work, a new journal developed by an international editorial team and hosted by the University of Calgary, Canada. In this Special Issue, we reflect on the COVID-19 pandemic as it relates to social work. We address considerations such as heightened inequities, experiences and recommendations that have emerged at this unique juncture in history across the world. Articles in this issue amplify COVID-19 considerations for practice, professional support, social work education, community development, advocacy, and the importance of addressing ongoing and emerging community and societal inequities, with a focus on nurturing well-being and equity. Articles offer reflection on individual, community and population experiences, including substantial shifts, societal chasms and considerations for social work and society in moving forward. We are grateful to article authors who have provided important reflections, learning and recommendations in moving forward. Amplifying the breadth of experiences and perspectives as we emerge from this local and global experience is important both to document this experience relative to social work, and to critically consider steps for moving forward.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".