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Record W6986162176

Oral History Interview with Ken Miller, February 18, 2005

2005· article· en· W6986162176 on OpenAlexaboutno aff

Bibliographic record

VenueThe Portal to Texas History (University of North Texas) · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicOral History, Memory, Narrative Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMillerOral historyNational guardOfficerWorld War IIMilitary serviceSpanish Civil WarNavy
DOInot available

Abstract

fetched live from OpenAlex

The National Museum of the Pacific War presents an oral interview with Colonel Ken Miller. Miller was born in Toronto, Canada and raised in California. As a student, he became interested in radio communications and went into the California National Guard in 1939. In the Guard he was wth 102nd Signal Radio Intelligence Company. Miller was on his way to Hawaii and then the Philippines when the Japanese attacked. The ship he was on returned to San Francisco and he was sent to Officer Candidte School. Upon graduating, he was assigned to the 8th Army Air Force Radio Squadron Mobile at Camp Pinedale where he trained units headed for the field overseas. Eventually, he shipped out with the last unit he trained and was stationed at Guam prior to the invasion of Iwo Jima. Once the island was secure, Miller went in and established his radio station where he intercepted Japanese radio traffic. Miller continues with several anecdotes about being on Iwo Jima: recovering Japanese code books from aircraft crashes; surfing; being attacked by Japanese planes; witnessing airplane ditches and crashes; receiving mail; being attacked at night by Japanese holdouts; working with Japanese Americans on Iwo Jima; a typhoon; etc. When the war ended, Miller was relieved and retuned to the US where he left the service only to join again and make a career out of it.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0550.002

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 head0.044
GPT teacher head0.203
Teacher spread0.160 · 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
Published2005
Admission routes1
Has abstractyes

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