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Record W4230952048 · doi:10.1353/gsp.2011.0071

Theorizing Destruction: Reflections on the State of Comparative Genocide Theory

2008· article· en· W4230952048 on OpenAlexaffvenue
Maureen S. Hiebert

Bibliographic record

VenueGenocide Studies and Prevention · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Contemporary Political Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGenocideState (computer science)Political scienceCriminologyEpistemologySociologyPhilosophyLawComputer science

Abstract

fetched live from OpenAlex

This article reviews the current state of comparative genocide theorizing, focusing on theories that attempt to account for the causes of genocide and the processes of genocidal killing.The literature is divided into three broad categories, based on the relative weight given to (a) individual or group agency, (b) structural factors, or (c) processes of identity construction in accounting for the origins and unfolding of genocidal destruction.The discussion of agencyoriented approaches focuses on theories that suggest that genocide is driven, in terms of decision making and perpetration, by elite decision makers, front-line perpetrators, and societal behavior.The literature on structural approaches is broken down into theories that stress the importance of culture, institutional organizations, societal cleavages, structural crises, regime type, modernity, and ideology.The final section reviews the literature on processes of collective identity construction.The article suggests throughout and in conclusion that although comparative genocide theorizing has come a long way in proposing a number of different explanations for the onset of genocide and the nature of genocidal processes, more work needs to be done with respect to the precise operationalization and testing of theories according to more rigorous comparative methodological practices.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.184
GPT teacher head0.329
Teacher spread0.145 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations6
Published2008
Admission routes2
Has abstractyes

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