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Record W1589843997

A Cautionary Tale from the Crusades? War and Prisoners in Conditions of Normative Incommensurability

2007· article· en· W1589843997 on OpenAlexaff
Frédéric Megret

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsMcGill University
Fundersnot available
KeywordsNormativeModerationLawLaw of warJust war theorySpanish Civil WarPolitical scienceCriminologySociologyPsychologySocial psychologyInternational law
DOInot available

Abstract

fetched live from OpenAlex

The conventional wisdom is that treatment of prisoners during the Crusades, in an age long before the development of the modern laws of war, can only have been horrendous. Historical evidence seems to partly vindicate this account, but is also testimony to how well prisoners were treated on some occasions. This chapter explores the impact that norms about the nature of war and how it should be waged had on the issue. It suggests that despite the religious intensity of the Crusades and the fact that they unfolded along civilizational divides, there were several strands within the Christian and probably even more the Moslem tradition which guaranteed a degree of moderation in warfare, particularly protection for prisoners. The conclusion is that an overarching law of war is not necessarily a pre-condition to restraint in warfare, as long as a number of other circumstances exist such as a strong deontological imperative of self-restraint.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.034
Scholarly communication0.0050.011
Open science0.0010.003
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.010
GPT teacher head0.279
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2007
Admission routes1
Has abstractyes

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