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Record W4390490985 · doi:10.31542/nysnyp21

The Crime of Crimes

2023· article· en· W4390490985 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCrossing Borders Student Reflections on Global Social Issues · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsMacEwan University
Fundersnot available
KeywordsGenocideIgnoranceCriminologyArgument (complex analysis)MainstreamContext (archaeology)SociologyLawPolitical scienceHistory

Abstract

fetched live from OpenAlex

Genocide is a topic that is almost universally ignored by criminology. While it is frequently referred to as “the crime of crimes,” there is virtually no criminological coverage of genocide. The following analysis is a review of existing criminological literature in genocide studies, situating mainstream criminology’s ignorance of genocide in a socio-historical context in order to determine the reason(s) for this disregard. This analysis proposes that the mainstream criminological ignorance of genocide is a calculated and intentional act. Such willful blindness avoids and deflects from disciplinary accountability because of criminology’s historical complacency in genocide. Most of the existing mainstream criminological literature on genocide is criticized because of its hyperfocus on definitional arguments, the redemptive nature of such academic coverage, and the quantification of such atrocities. Thus, an argument for a critical criminological approach to genocide studies is desperately needed for criminology to interpret genocidal acts adequately.

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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
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 head0.072
GPT teacher head0.461
Teacher spread0.389 · 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