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Record W7061665490

Rethinking access to racial justice: Race discrimination and First Nations peoples

2021· article· en· W7061665490 on OpenAlexaboutno aff

Bibliographic record

VenueUTS ePRESS (University of Technology Sydney) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGyrotron and Vacuum Electronics Research
Canadian institutionsnot available
Fundersnot available
KeywordsRacismIndigenousRace (biology)Economic JusticeMainstreamJurisdictionFace (sociological concept)White (mutation)Human rights
DOInot available

Abstract

fetched live from OpenAlex

Aileen Moreton-Robinson, a Goenpul woman from the Quandamooka people, asserts that the 'official' story about racism in Australia is that it exists only in 'small pockets of society or not at all'.First Nations peoples, she claims, tell quite a different story about where, when and how this problem manifests, identifying it as a significant issue with substantial personal and whole-of-community impacts. 1 Various studies and surveys confirm this.As an example, in a 2016 Northern Territory research project, Telling it like it is: Aboriginal perspectives on race and race relations, 64 per cent of the Aboriginal participants reported 'never' or 'rarely' being treated the same as non-Aboriginal people. 2 The Indigenous Legal Needs Project (ILNP) was a national research project looking at First Nations' access to justice in civil and family law areas that ran from 2011-2015.Depending on the jurisdiction in which they lived, between 28 per cent and 41 per cent of ILNP participants identified having encountered race-based discrimination in the previous two years.3 Discrimination was described by participants as long-standing and omnipresent, suffered 'at least once a day, every day'.'You're going to face it no matter where you are … at work, at home, school, wherever'.4 First Nations peoples' experiences of discrimination range from the interpersonal to the institutional, encompassing blatant racial abuse in public spaces, under-and over-policing, being unable to obtain a private tenancy, being unable to access or to retain paid work, as well as having interactions with mainstream systems that continually fail to accommodate cultural and other needs and perspectives.5

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)0.014
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.113
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0350.047
Scholarly communication0.0110.020
Open science0.0030.021
Research integrity0.0090.021
Insufficient payload (model declined to judge)0.0060.000

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 head0.022
GPT teacher head0.288
Teacher spread0.266 · 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 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
Published2021
Admission routes1
Has abstractyes

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