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Record W6995476513

Oral History Interview with Robert Rhoades, May 7, 2009

2009· article· en· W6995476513 on OpenAlexaboutno aff

Bibliographic record

VenueThe Portal to Texas History (University of North Texas) · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOral historyNavyVictoryNew guineaWorld War IIWhite (mutation)Coast guardGeorge (robot)
DOInot available

Abstract

fetched live from OpenAlex

The National Museum of the Pacific War presents an oral interview with Robert Rhoades. Rhoades joined the Navy in February 1942 and received basic training in San Diego. He received gunnery training at an armed guard school and upon completion was assigned to a Merchant Marine ship, the SS Canfield, carrying supplies to Halifax. He then served aboard the SS Potomac, bringing Army supplies to Panama and returning with raw sugar. The ship was coal-powered and broke down in the Gulf of Mexico; so upon return, Rhoades asked to be transferred. He went aboard the SS Jose Bonifacio (USAT-907), a Liberty ship built by Kaiser, with a load of Army jeeps for Perth. He then brought a P-51 to Calcutta, where poverty and differences in culture left a lasting impression on him. The ship brought a load of 300 monkeys back to New York City for research experiments, and Rhoades was transferred to the MV Cape Matapan (C1-A-292), which brought supplies to Army personnel in Chile. The last ship he served on was the SS Sapulpa Victory (V-14), taking small arms and ammunition to New Guinea and bringing bombs to the Marianas. He was then stationed on Ulithi and witnessed the kamikaze strike against the USS Randolph (CV-15). Rhoades returned home and served at a hydrographic office in New Orleans until his discharge in October 1945.

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)0.001
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.131
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.1310.038

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 head0.035
GPT teacher head0.202
Teacher spread0.167 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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