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Record W16964838 · doi:10.15173/nexus.v16i1.185

Multiplicity of Balinese Characters

2003· article· en· W16964838 on OpenAlexaffvenue
Sonya de Laat

Bibliographic record

VenueNEXUS The Canadian Student Journal of Anthropology · 2003
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMisrepresentationReflexivitySociologyPhotographyEpistemologyVisual researchOrder (exchange)Comparative historical researchAnthropologyAestheticsVisual artsArtLawPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Photography as main research data has not been used in anthropology to the extent that it was by Margaret Mead and Gregory Bateson in the late 1930s. This paper takes a critical examination of their use and analysis of photographs in their research in Highland Bali, to demonstrate that their subsequent conclusions may be misrepresentative of the people they were studying. By reviewing analyses by Ira Jacknis and Gerald Sullivan, along with more historical and theoretical considerations, it will become apparent that although the couple exhaustively used the photographic medium, their analysis and conclusions seem to have been manipulated to suit their original hypothesis. Their conclusions being drawn from a small portion of the unprecedented corpus of material, the bias from one of their funding bodies and their lack of collaborative analysis with the research subjects may have been the causes of this possible misrepresentation, although further research would be needed in order to support this claim. The paper concludes with a brief analysis of how the Mead and Bateson project should be viewed by contemporary students of visual anthropology, specifically with respect to reflexivity, collaboration. and contemporary ethical considerations. Finally, the paper calls for further research to be done with this material.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.287
Teacher spread0.205 · 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.

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
Published2003
Admission routes2
Has abstractyes

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