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Record W2556765297 · doi:10.7152/jipa.v36i0.14913

AN INTERDISCIPLINARY APPROACH TO THE STUDY OF BOATS OF CENTRAL VIETNAM

2016· article· en· W2556765297 on OpenAlexaff
Charlotte Minh Hà Pham

Bibliographic record

VenueJournal of Indo-Pacific Archaeology · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsMcGill University
Fundersnot available
KeywordsChinaContext (archaeology)EthnographyAgrarian societyMaritime historyState (computer science)GeographyDiversity (politics)EconomyHistoryArchaeologySociologyAnthropology

Abstract

fetched live from OpenAlex

Despite a growing academic literature on maritime trade, shipping and navigation in the South China Sea, there is little information about how local societies negotiated their maritime environment, or how it influenced their daily life. This is most particularly the case for Vietnam, often considered through its history as an agrarian state. Nonetheless, with a coastline of over 3400 km located along a major shipping route between Malacca and China, Vietnam has a long lasting historical connection with its maritime environment and an exceptional boat diversity. Yet again, little is known about local boatbuilding traditions, boat use, seafaring skills and navigation, related maritime activities, about the organisation and role of the many harbours that dotted the coast of central Vietnam.As a step in the development of maritime archaeology in Vietnam, a combined approach in the research of archives and ethnography can contribute to build up knowledge about maritime aspects of life in Vietnam, and can also provide context and understanding for potential maritime archaeological finds. At the same time it can push the boundaries of maritime archaeologists to incite research that goes beyond nautical technology.

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)0.002
metaresearch head score (Gemma)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.004
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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