The COVID-19 Crisis: Using the Cracks in Neoliberalism for Social Transformation Towards a More Just Society
Bibliographic record
Abstract
Within the current COVID-19 pandemic, cracks observed in neoliberal dominant global economic paradigms reveal how austerity policies have crippled crucial social safety nets, such as health care, with capitalism continuing to adversely impact our climate with ad infinitum extraction of resources for overconsumption. In examining these associations, this collaborative paper critically applies social theories to explore ideas and approaches to creating transformative social change, in an effort to move towards a more just and sustainable society in the context of the COVID-19 pandemic and other ongoing systemic crises. The paper presents the pandemic as a social crisis and explores theories of social justice and how they might be applied within the context of neoliberal capitalism, also known as neoliberalism. The authors of this paper argue that to move towards a just society, social transformation is needed, informed by the theories of decoloniality and intersectionality. A conceptual model is presented that demonstrates how these theories can be woven together to inform community psychology action and research, addressing COVID-19 specifically. Possibilities for transformation in the areas of mental health and climate justice are also presented. Finally, recommendations for community psychology researchers seeking social transformation, while navigating this challenging and complex new reality, are shared.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".