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Record W4403893934 · doi:10.1002/arco.5343

Aboriginal flood narratives and the thunder complex in Southeast Asia

2024· article· en· W4403893934 on OpenAlexaff
Adam Brumm, Gregory Forth

Bibliographic record

VenueArchaeology in Oceania/Archæology & physical anthropology in Oceania · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsThunderNarrativeFlood mythSoutheast asiaGeographyHistoryEthnologyArchaeologyMeteorologyArtLiterature

Abstract

fetched live from OpenAlex

Abstract The ancestors of the dingo were brought to mainland Australia around 4000 years ago by people who arrived by boat. The identity of these voyagers from the north, however, and the nature of their interactions with the Aboriginal population of Australia, are unknown. Here, we propose that Indigenous flood narratives from the Kimberley contain evidence for contact between Aboriginal people and early Asian seafarers in the form of the “thunder complex”. The latter is a very specific repertoire of taboos, rituals and stories that occurs widely among ethnographically known societies of Indonesia, the Philippines and peninsular Malaysia, but has not previously been identified in Australia. Among Southeast Asian groups, this cultural complex revolves around the idea that certain prohibited acts perpetrated against animals – especially “mocking” them by treating them as though they were human – precipitate a punitive storm and/or flooding. We show that in some oral traditions of the Kimberley region animal mockery is similarly held to be the causative agent behind disastrous flooding events that took place in the past creationary epoch. We contend that this localised Aboriginal variant of the thunder complex reflects an episode of close interaction with early Austronesian‐speaking voyagers who introduced ancestral dingoes to mainland Australia, apparently via the Kimberley coast.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.019
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.280
Teacher spread0.273 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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