The politics of abolition: Reframing the death penalty's history in comparative perspective
Bibliographic record
Abstract
Literature on opposition to the death penalty typically characterizes abolition as inexorable and attributes its fulfillment to the age of human rights. Although most countries abolished capital punishment after the Universal Declaration of Human Rights in 1948, this article uses three comparative case studies to demonstrate abolition’s entanglement with a broader range of political, legal, and cultural factors. Applying a historically grounded nonteleological approach, we offer three insights. First, civilizationist values drove abolitionism in countries in the “vanguard,” such as Canada and England/Wales, where human rights rationales were expressed well after abolition and as a mark of superiority. Second, death penalty abolition has often allied with decolonization and penal reform, but assertions of independence and sovereignty have periodically provoked reinstatement, as in Mexican and Philippine history, which underscores the fragility of abolition. Third, state-centric approaches to de jure and de facto abolition overlook the practice of extrajudicial and summary “rebel” executions in polities such as Myanmar and Mali, which lack a state monopoly on force. Further historical studies that do not presuppose a human rights explanation of abolition and that compare jurisdictions within as well as between the Global North and South will better grasp the death penalty’s complex history.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.059 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| 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".