MétaCan
Menu
Back to cohort
Record W4404916963 · doi:10.25071/2563-2418.116

Di Pedalaman Sulawesi

2024· article· en· W4404916963 on OpenAlexvenueno aff
Walter Kaudern

Bibliographic record

VenueLOBO Annals of Sulawesi Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Walter Kaudern (1881-1942) was a Swedish Zoologist and Ethnographer who made a research trip to North and Central Sulawesi between 1917 and 1921. The trip began with a focus on Zoological questions largely pursued in North Sulawesi, but quickly shifted to ethnographic concerns in Central Sulawesi. Here, he worked for extensive periods in Kulawi, and Loinang, with comparative side excursions in Pipikoro, Bada' and Ondae, collecting an extensive 3000 artifact collection which now resides in the Swedish Museum of World Culture (Gothenburg). He published the results of his fieldwork in English in an extensive 6 volume "Kajian Ethnografi di Sulawesi: Hasil Ekspedisi Penulis ke Sulawesi 1917-1920 (1925-45), several volumes of which have already been translated in LOBO. Di Pedalaman Sulawesi, however, is an initial survey work published in Swedish and thus largely inaccessible. The importance of this work is not its reports of zoological and ethnographic findings which are reported at length elsewhere, but its intimate personal reflections on his research and methods, and in particular, his interactions with the Dutch colonial government, the Salvation Army Missionaries, and especially the inhabitants of Central Sulawesi. It is not a flattering self-portrait. This work will be published in three volumes, and contains extensive links to the collection of photographs available from the Museum of World Culture. Readers are warned that this work contains racist language.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.003

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 head0.219
GPT teacher head0.440
Teacher spread0.221 · 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; both teacher heads agree on what is shown here.

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueLOBO Annals of Sulawesi ResearchSame topicCoastal Management and DevelopmentFrench-language works237,207