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Record W3090667864 · doi:10.3138/cart-2019-0020

Mapping Colonial Massacres and Frontier Violence in Australia: “the names of places”

2020· article· en· W3090667864 on OpenAlexvenueno aff
Greg Hooper, Jonathan Richards, Judy Watson

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsFrontierColonialismIndigenousSovereigntyAnnexationResistance (ecology)PoliticsState (computer science)DenialHistoryGeographyPolitical scienceLawEcology

Abstract

fetched live from OpenAlex

European hunger for resources, particularly land, led to the denial of Indigenous sovereignty and culture with the expansion of European colonialism. This was the most important ecological, economic, and political upheaval to affect the modern world. Records created during this violent rush of land-hunting and flag-planting can help us to understand how Western empires carved up “new” worlds into tributary states and discrete spheres. In Australia, persistent Indigenous resistance to European rule led to the establishment of the Native Police – an armed, mobile paramilitary force – consisting of Aboriginal troopers led by European officers. The sole purpose of the Native Police was to crush this resistance. Drawing upon historical documents and maps from the European invasion and annexation of territory, this article discusses the “names of places” project, which involves the mapping of colonial massacres and frontier violence in Queensland, Australia, including the mapping of the actions of the Native Police in the northeastern part of the Australian continent. In particular, we interrogate surviving records, currently held at the Queensland State Archives, which illustrate the actions, the composition, and the movements of this colonial formation. Mapping colonial violence forces readers to acknowledge that empires and modern nations are often built on the bones of the dispossessed.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.313
Teacher spread0.286 · 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 designObservational
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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCartographica The International Journal for Geographic Information and GeovisualizationSame topicAustralian History and SocietyFrench-language works237,207