COVID-19, Health Justice, and the Privilege of Space: A New Critical Intersectional Framework for Creating a Prescription for Equal Well-Being and Applied to Addressing Health of Children Residing in Psychiatric Institutions
Bibliographic record
Abstract
Our nation - founded on life, liberty, and the pursuit of happiness - is a year into the COVID-19 pandemic. The pandemic has revealed the gap between what we are as a society and that which we long to be. A new critical intersectional legal framework, guided by Dr. Martin Luther King Jr.’s vision of The Beloved Community, will allow legal scholars and policymakers to reframe health equality and health justice toward a more perfect union. By combining the philosophical rigor of dialectical thinking, critical theory, and intersectional analysis, analysts can meet this moment and create new legal frameworks to correct social injustice. Analysts can build a just society based on equality to address the disproportionate sickness, disability, and death of America’s historically oppressed peoples. With the goal of addressing oppression across multiple axes of identity at once, and in the spirit of Dr. King’s appropriation of eclectic theologies and philosophies, this Article proposes a new Critical Intersectional Legal Analysis that develops critical social theory by bringing an intersectional analysis to the principles of dialectical thought and indeterminacy. This Article’s framework will analyze power structures as they exist and work together through the power of the state to class, race, and disable people moment to moment. Finally, this Article’s framework is reconstructive through self-reflexive application of theory through praxis. This Article will apply that new framework to a specific condition of oppression - the privilege of space as it relates to the risks of viral transmission, infection, and disease during the current coronavirus pandemic for children in psychiatric institutional settings in North Carolina and the Southeast.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.018 | 0.134 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".