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Record W7038327493

On genocide and settler-colonial violence: Australia in comparative perspective

2016· other· en· W7038327493 on OpenAlexaboutno aff

Bibliographic record

VenueNOVA (University of Newcastle Australia) · 2016
Typeother
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsnot available
Fundersnot available
KeywordsGenocideColonialismIndigenousFrontierCausationContext (archaeology)PoliticsNexus (standard)
DOInot available

Abstract

fetched live from OpenAlex

The use of the term 'genocide' as a model for explaining frontier violence has generated varying degrees of scholarly debate and public interest among former British colonial settler societies as they attempt to come to grips with the heritage of their colonial pasts. The political and moral stakes at play are important, for at the core of the debates there often lie questions of national identity. In some former colonial societies, such as Australia, sections of the public, however small, have entered the debate, including ·politicians and journalists, not to mention spokespeople of the Indigenous communities who were the past victims of colonial settler societies (and in some ways continue to be). Almost everywhere though, the debates have become polarized between Whiggish views of settler colonialism that see it as an essentially positive, civilizing process, and those who point to the displacement of Indigenous peoples, their death and often their disappearance. The term 'genocide' is used by some scholars of the colonial frontier, but is rejected by others. The purpose of this chapter is not to discuss whether genocide occurred in settler-colonial societies, nor is it to engage in debates about causation and intent. Rather, our purpose is to place the Australian debates around genocide and settler-colonial society in a larger context by comparing them with other, similar settler societies, such as South Africa, the USA, Canada and New Zealand. In doing so, we want to examine the ways in which genocide discourse has been used to make sense of the traumatic pasts in these regions of the world, and how the debates have become part of the discussions about national identity.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.463

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0130.011
Scholarly communication0.0060.005
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.132
GPT teacher head0.362
Teacher spread0.230 · 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 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

Citations2
Published2016
Admission routes1
Has abstractyes

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