Bibliographic record
Abstract
HIGHLY CONTENTIOUS ISSUES EMERGE IN CONNECTION WITH POLITICAL VIOLENCE; among these are the innocence of victims, political obligation, as well as rights and rights violations. This article attempts to deal with the issue of legitimacy as that issue has taken shape in the violent conflicts between terrorist organizations and states. Beginning with a review of the social scientific literature, and proceeding to address Max Weber's ideas about the social and psychological bases for legitimacy, I end with an appraisal of J?rgen Habermas' views. Along the way, a variety of questions is raised: In what ways has legitimacy been contested? How is legitimacy defined? Under what conditions may legitimacy be ascribed to states or terrorist organizations? In what does their legitimacy consist? It should be stated from the outset that these questions are posed on a more general, philosophical level; no judgments about the legitimacy of particular states or organizations are made. However, it is hoped that this examination of legitimacy will make a modest contribution to contemporary debates. Since political violence today often revolves around the issue of legitimacy, this issue requires much closer scrutiny than it has received in the existing social scientific and philosophical literature. For that reason, this article seeks to broach a more critical examination of the notion of legitimacy.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.006 | 0.080 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".