Bibliographic record
Abstract
BACKGROUND: This paper has its origins in Jonathan Mann's insight that the experience of dignity may explain the reciprocal relationships between health and human rights. It follows his call for a taxonomy of dignity: "a coherent vocabulary and framework to characterize dignity." METHODS: Grounded theory procedures were use to analyze literature pertaining to dignity and to conduct and analyze 64 semi-structured interviews with persons marginalized by their health or social status, individuals who provide health or social services to these populations, and people working in the field of health and human rights. RESULTS: The taxonomy presented identifies two main forms of dignity-human dignity and social dignity-and describes several elements of these forms, including the social processes that violate or promote them, the conditions under which such violations and promotions occur, the objects of violation and promotion, and the consequences of dignity violation. Together, these forms and elements point to a theory of dignity as a quality of individuals and collectives that is constituted through interaction and interpretation and structured by conditions pertaining to actors, relationships, settings, and the broader social order. CONCLUSION: The taxonomy has several implications for work in health and human rights. It suggests a map to possible points of intervention and provides a language in which to talk about dignity.
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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 | 0.055 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| 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".