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Record W2048599130 · doi:10.5172/conu.2006.22.2.169

Issues of language across the cultural [and colonial] divide

2006· article· en· W2048599130 on OpenAlexaffabout
Eileen Willis, Maria Rameka, Victoria Smye

Bibliographic record

VenueContemporary Nurse · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicNames, Identity, and Discrimination Research
Canadian institutionsCanadian Institutes of Health ResearchUniversity of British Columbia
Fundersnot available
KeywordsNeocolonialismIndigenousProject commissioningPoliticsMeaning (existential)RacismColonialismPublishingIdentity (music)SociologyMedia studiesGender studiesPolitical scienceLawPublic relationsPsychologyAesthetics

Abstract

fetched live from OpenAlex

How people come to name themselves is an important act of identity. How others name them is a political act. For example, names used for 'First Nations' people may be an act of respect as they become more aware of the political meaning (and sometimes racist intention) embedded in language, or yet another form of neocolonialism. In addition, what is appropriate 'naming' shifts over time. As an example, in Australia, Canada and New Zealand, this shift has in recent times been part of the struggle of many Indigenous peoples for self- determination and for the elimination of racism. In addressing these issues, the editors wish: 1. to alert readers to the fact that there are differences across the three countries that might cause offence, but are in fact appropriate in the country of origin; and, 2. to point out to readers where authors have made judicious decisions about the use of language. (non-author abstract)

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)0.036
metaresearch head score (Gemma)0.069
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.069
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0200.050
Scholarly communication0.0210.029
Open science0.0040.010
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0100.001

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 head0.055
GPT teacher head0.422
Teacher spread0.367 · 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

Citations3
Published2006
Admission routes2
Has abstractyes

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