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Record W4243900423 · doi:10.1386/nzps_00035_1

Transcending the colonial gaze: Empathy, agency and community in the South Pacific photography of John Watt Beattie1

2020· article· en· W4243900423 on OpenAlexaff
Terry M. Brown

Bibliographic record

VenueJournal of New Zealand & Pacific Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsTrinity College
Fundersnot available
KeywordsColonialismBeautyEthosContext (archaeology)GazeAgency (philosophy)StudioHistoryPhotographyMedia studiesVisual artsArt historySociologyArtArchaeologyAestheticsPsychologyPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

For three months in 1906, John Watt Beattie, the noted Australian photographer – at the invitation of the Anglican Bishop of Melanesia, Cecil Wilson – travelling on the church vessel the Southern Cross , photographed people and sites associated with the Melanesian Mission on Norfolk Island and present-day Vanuatu and Solomon Islands. Beattie reproduced many of the 1500-plus photographs from that trip, which he sold in various formats from his photographic studio in Hobart, Tasmania. The photographs constitute a priceless collection of Pacific images that began to be used very quickly in a variety of publications, with or without attribution. I shall examine some of these photographs in the context of the ethos of the Melanesian Mission, British colonialism in the Solomon Islands, and Beattie’s previous photographic experience. I shall argue that Beattie first exhibited a colonial gaze of objectifying his dehumanized exotic subjects (e.g. as ‘savages’ and ‘cannibals’) but with increased familiarity with them, became empathetic and admiring. In this change of attitude, I argue that he effectively transcended his colonial gaze to produce photographs of great empathy, beauty and longevity. At the same time, he became more critical of the colonial enterprise in the Pacific, whether government, commercial or church.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.029
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.107
GPT teacher head0.332
Teacher spread0.225 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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