Bibliographic record
Abstract
In the growing field of the sociology of human rights, the notion that human rights might best be understood as the expansion and/or supersession of citizenship rights has taken root, as has the more generalized taken for granted “backstories” to the effect that human rights are the product of a unique postwar consensus. In this article, I argue that these assumptions are more encumbrance than assistance when it comes time to sociologically grasping what human rights are, how they emerged, and, more importantly, what they might be able to achieve. In the first half of the article, I demonstrate that the tropes of expansion and supersession of citizenship rights are central to two seminal sociological analyses of human rights—those of Bryan S. Turner (1993, 2006) and Yasemin N. Soysal (1994, 2012) —and that they fail to provide a social-relational and historical account of the emergence of human rights. In the second part, I pull together new historical and sociolegal scholarship that is recalibrating our understanding of human rights. Drawing attention to the 1970s as the more persuasive social-relational origin of contemporary human rights, I argue, allows a more nuanced appraisal of human rights’ (in)efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".