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Record W2624870089 · doi:10.1093/ahr/122.3.830

David A. Chang. The World and All the Things upon It: Native Hawaiian Geographies of Exploration.

2017· article· en· W2624870089 on OpenAlexaff
Jean Barman

Bibliographic record

VenueThe American Historical Review · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNative HawaiiansIndigenousNewspaperHistoryNative americanGenealogyEthnologyMedia studiesSociologyAnthropologyEthnic groupPacific islandersEcology

Abstract

fetched live from OpenAlex

In The World and All the Things upon It: Native Hawaiian Geographies of Exploration, David A. Chang recovers the history and outlook of indigenous Hawaiians or Kānakas, with whom he identifies himself. The title, which exemplifies the book’s central point, is taken from a serialized geography of the world by Kānaka teacher and intellectual J. H. Kānepu’u, which was published in a Hawaiian-language newspaper in 1877 (103). Long before the Hawaiian Islands were “discovered” in 1778 by English sea captain James Cook, the Kānakas knew who they were, just as they still do. It is outsiders who have taken so long to figure it out. Far too many of us still take for granted the United States’ overthrow of an independent people, which led to the Hawaiian Islands being annexed in 1898, rather than seeking to understand indigenous Hawaiians as active participants in the course of events. Kānakas not only acted but also recorded their actions and those of their predecessors. An American missionary presence early on meant that “by the late 1830s, most Kānaka were literate” (ix). Chang’s fluency in the Hawaiian language and his incorporation of Hawaiian-language materials into the text is innovative. While a small handful of scholars, such as Noenoe K. Silva, emphasize Native voices, more histories need to be written from the Kānaka perspective.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.142
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.002
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.002

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 head0.070
GPT teacher head0.284
Teacher spread0.214 · 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 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
Published2017
Admission routes1
Has abstractyes

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