Looking into the kaleidoscope of activism: the engagement of care ethics and global bioethics for a refined health security
Bibliographic record
Abstract
During public health crises, the United States utilizes a statist approach for securing its population’s health, which places state structures at the center of a (mainly economic) health security. The fairness of this approach relies on a distribution of resources to “trickle down” from institutions to individuals. Yet, “fairness,” in this regard, is determined a priori, that is, without reference to specific individuals who are receiving resources of health. This ignores contextual needs that arise from the disproportionate damage that epidemics and pandemics have on vulnerable populations. A statist approach can make a more equitable impact on global society if it integrates care ethics into its distributive justice. In this paper, I demonstrate how an ethic of care can substantiate health security. First, I show how an ethic of care can be engaged anywhere embodiment is recognizable—not just in the one-on-one setting of the clinical encounter—but in the (inter)national contexts through which public health crises have a full effect on. Second, I provide a methodology for state institutions to recognize the social embodiment necessary to engage an ethic of care in these contexts, specifically engaging the social embodiment that manifests through the social activism of vulnerable populations during public health crises. Third, I demonstrate how the social embodiment that activism lives through forces an encounter with state institutions, mimicking in this manner a clinical encounter on a macrocosmic scale. Finally, I assign an ethic of care to this encounter, meshing caring values to the criteria of distribution.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.124 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".