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Record W1869331324 · doi:10.3138/topia.22.5

Asking Too Much, Receiving Too Little: Indigenous Identity and the Aims of Science

2010· article· en· W1869331324 on OpenAlexvenueno aff
Adam Müller

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousBioprospectingSuperstitionEpistemologyVariety (cybernetics)Identity (music)Environmental ethicsSociologySociology of scientific knowledgeSocial scienceHistoryComputer sciencePhilosophyAestheticsEcologyArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper I examine the refusal by many indigenous people to participate in the National Geographic Society’s Genographic Project, and take a close look at a variety of scientific and indigenous responses to that refusal. I locate in many scientific reactions an often implicit reliance upon what I term the “Platonic Conceit”: that at the centre of what it means to be human there lies an ineluctable desire for the truth and an aversion to simulations. This Conceit blinds scientists to the extent to which some simulations not only matter deeply to people but enable their world to make sense. It also prevents scientists from easily distinguishing between knowledge and superstition in so-called traditional knowledge schemes, and therefore from adequately understanding the motivation and substance of indigenous complaints about “bioprospecting.”

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)0.018
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0230.079
Scholarly communication0.0080.008
Open science0.0010.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.283
Teacher spread0.267 · 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.

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

Citations1
Published2010
Admission routes1
Has abstractyes

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