Bibliographic record
Abstract
One of the defining features of the capability approach (CA) to health, as developed in Venkatapuram's book Health Justice, is its aim to enable individual health agency. Furthermore, the CA to health hopes to provide a strong guideline for assessing the health-enabling content of social and political conditions. In this article, I employ the recent literature on the liberal concept of vulnerability to assess the CA. I distinguish two kinds of vulnerability. Considering circumstantial vulnerability, I argue that liberal accounts of vulnerability concerned with individual autonomy, align with the CA to health. Individuals should, as far as possible, be able to make health-enabling decisions about their lives, and their capability to do so should certainly not be hindered by public policy. The CA to health and a vulnerability-based analysis then work alongside to define moral responsibilities and designate those who hold them. Both approaches demand social policy to address circumstances that hinder individuals from taking health-enabling decisions. A background condition of vulnerability, on the other hand, even though it hampers the capability for health, does not warrant the strong moral claim proposed by the CA to health to define health as a meta-capability that should guide social policy. Nothing in our designing social policy could change the challenge to health agency when we deal with background conditions of vulnerability.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.084 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| 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".