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Record W6945717321 · doi:10.25446/oxford.25924189.v1

The Short Military Career of my Uncle, David Andrew Hubbell

2024· other· en· W6945717321 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAsideInfantryClearanceSpanish Civil WarEstatePrisoners of warWorld War IIBattlefield

Abstract

fetched live from OpenAlex

My uncle David Andrew Hubbell was 17 when he signed up for the Royal Canadian Air Force. He wanted to be an aerial gunner and trained at night at a local high school auditorium in Toronto. However in early 1945 he was quietly taken aside by the C.O. at the training centre and told that it appeared that the war was winding down and the RCAF wouldn't be needing him anymore as a gunner. But, added the officer, with the ongoing war against Japan, the Army was short of manpower and was looking for volunteers. According to the official record, David wasn't terribly happy leaving the RCAF, but decided to join the Canadian Army and volunteered to take part in the planned1946 invasion of Japan as part of the Canadian contingent. He signed up and there are photos of him in training at Camp Ipperwash on the shores of Lake Huron (now a popular provincial park, although the former military camp is off limits in places as ordnance is still being cleared from there, at least as of 2020). The war in Europe ended and I believe he was transferred to Niagara for further training but when Japan surrendered later in the year he apparently went AWOL to celebrate. (Apparently this went unpunished, so I am guessing the whole camp probably emptied out). He was demobbed either late 1945 or early 1946 and returned to civvy street, where he had hoped to become -- like his father -- a real estate broker, although he initially took a job as a delivery boy for a local butcher. However, while on the job he complained of feeling weak and fatigued, and thought he was low on iron, and so started a diet of organ meat from the butcher's he was working at but it didn't help. Taken to a doctor he was diagnosed with leukaemia. The doctor told his parents that surgery could be done but it would only be a temporary fix (if it even worked) and he shortly would be in even worse condition than he presently was. I don't think David was told that, to spare him, for good or for bad. He took a sudden turn for the worse in early December and died of an internal haemorrhaghe shortly before Xmas, 1946. He was all of 20, only a few weeks away from turning 21. He is buried in Toronto's Mount Pleasant cemetery alongside his Mother and Father.

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)0.010
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: Other · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.003
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0480.026

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 head0.045
GPT teacher head0.247
Teacher spread0.201 · 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
GenreOther

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

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