Bibliographic record
Abstract
The COVID-19 pandemic represents a critical juncture in the development of the welfare state affirming its importance for its citizens’ economic, health and wellbeing, and safety, especially for its most vulnerable populations. It demonstrated that the crisis preparedness that is crucial for an effective protection of its citizens, the ultimate purpose of the welfare state, unquestionably exceeds the narrow horizon of a corporatised welfare industry with its singular focus on the maximisation of profit for the elites and cost containment for the government. Social workers need to engage with the contradictions and tensions that spring from underfunded welfare services and engage in the political struggle over a well-resourced welfare state. Contributors to this book take on this challenge. By tracing the various contradictions of the pandemic, the contributors reflect on new ways of thinking about welfare by exploring what to keep, what to challenge and what to change. By highlighting important challenges for a social justice-focused response as well as exploring the many challenges exposed by the pandemic facing social work for the coming decades, contributors critically outline pathways in social work that might contribute to the shaping of a less cruel and more capable welfare state. Using case-studies from Indigenous and non-Indigenous Australia, Italy, Slovenia, Estonia, Sweden, Spain, South Africa, Canada, Sri Lanka, Zimbabwe, China and the United States, the book features 19 chapters by leading experts. This book will be of interest to all social work scholars, students and practitioners, as well as those working in social policy and health more broadly. © 2024 selection and editorial matter, Goetz Ottmann and Carolyn Noble; individual chapters, the contributors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".