Towards a Feminist Geo-legal Ethic of Caring Within Medical Supply Chains: Lessons from Careless Supply During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 crisis illustrates the fragility of supply chains. Countries with excellent health systems struggled to ensure essential supplies of food, medicines, and personal protective equipment which were vital to a fast and effective response. Using geo-legality, which maps the constitutive relations between law and space, we argue that the failure of supply chains in many western countries during the crisis reveals a fundamental tension between their role as facilitators of care and caring, and the logistic logics by which they operate. While supply chains link the intimate, domestic concerns of providing medical care with the globalised geographical concerns of moving goods across different jurisdictions at the right time, their contemporary organisation and regulation does not reflect the caring relations and public goods they are meant to support. Drawing on analysis of examples from Canada, the United Kingdom, and the United States, this article argues that a reconfiguration of supply chains in accordance with feminist approaches that place care at the centre of supply chain operation and organisation will be important to amendments of both domestic and global health law.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.100 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 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".