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Record W4306983710 · doi:10.21039/jpr.4.2.114

Cultural Genocide in Joe Sacco's Paying the Land

2022· article· en· W4306983710 on OpenAlexaboutno aff
Johannes C. P. Schmid

Bibliographic record

VenueJournal of Perpetrator Research · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousGenocideNarrativeColonialismPolitical scienceCriminologyAccountabilityDepictionHistorical traumaGender studiesSociologyGeographyLawPsychologyArtVisual arts

Abstract

fetched live from OpenAlex

This article explores the representation of cultural genocide in the case of Canada’s Indigenous peoples in Joe Sacco’s documentary graphic narrative Paying the Land, which focuses on the Indigenous Dene peoples in the Canadian Northwest Territories. Specifically, the article discusses Sacco’s depiction of perpetrators of the so-called Indian Residential School System (IRSS), which is contrasted with portrayals of intracommunal violence and Indigenous perpetrators. Through graphic narrative means, Paying the Land presents the latter as an aftereffect of the former and extensively explores how cycles of domestic violence and substance abuse were initiated through the attempted destruction of Indigenous peoples as a group, a process in which the residential schools played an important role. In doing so, Sacco specifically addresses a North American audience as implicated subjects who, like himself, are entangled in settler-colonial histories. He investigates the complexities of perpetratorship and accountability that involves not only the policymakers and residential school staff but also North American society at large. In respect to intracommunal violence among the Dene, Paying the Land seeks to shift public perception from inherently ‘deficient’ Indigenous culprits toward an understanding of the colonial policies that have purposefully eroded social cohesion among Indigenous peoples.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.159
GPT teacher head0.372
Teacher spread0.213 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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