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Record W4214705247 · doi:10.1177/12063312211073048

The Blacktown Native Institution as a Living, Embodied Being: Decolonizing Australian First Nations Zones of Trauma Through Creativity

2022· article· en· W4214705247 on OpenAlexaboutno aff
Brook Andrew, Lily Hibberd

Bibliographic record

VenueSpace and Culture · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsMemorializationSociologyInstitutionPerformative utteranceColonialismContext (archaeology)EthosLand tenureGender studiesEthnologyAestheticsLawPolitical scienceHistoryArchaeologySocial science

Abstract

fetched live from OpenAlex

In Australia, the trauma of the forced removal, institutionalization, and attempted assimilation of Aboriginal and Torres Strait Islander children under Stolen Generations policies is rarely publicly memorialized, especially at the children’s homes and missions where these things took place. Darug Nation reclamation of the former site of the Blacktown Native Institution in Western Sydney entails, however, a distinct memorialization of the land as a powerful identity through restoring ceremonial and land care cultural practices that predate invasion. The Darug activation of this place pivots on a powerful Aboriginal ethos of land as “Country”—a living being or spirit. We also contend that this relationship to land is better defined by the expansive term “zone” rather than the colonial, territorial notion of “site.” It is in this context that Darug Traditional Owners, other First Nations artists, and Stolen Generations survivors are generating remarkable artistic, communal, ephemeral, land-based, and performative approaches that empower and restore Darug bonds, with the land of the former institution as a living being.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.036
Scholarly communication0.0070.004
Open science0.0010.014
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.317
Teacher spread0.297 · 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 designQualitative
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

Citations8
Published2022
Admission routes1
Has abstractyes

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