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Record W6908133765 · doi:10.25446/oxford.25927633

Jack Warren and my Oxford Childhood

2024· other· en· W6908133765 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Oxford · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAuntScreamingBrotherGirlChampionSisterMidnightChapelGerman

Abstract

fetched live from OpenAlex

My father worked at Osberton Radiators, developing tanks for the military. He cut his finger at work and was admitted to the Radcliffe Infirmary, then transferred to the Slade. Only my mum was permitted to visit him and he died shortly afterwards, aged 30 years (died 8.12.1941). He was a good snooker player and he played against the world champion in London. Shortly after the start of the war, we had an evacuee girl from London. She was covered in lice and fleas, and could swear! We lived in a middle-class area of Oxford. She tended to wander off and, one day, she went to the field between our house and Botley Crematorium, where there were giant elm trees. She ran back screaming because she thought she had seen a giant dog. It was a cow! In 1943, mother took us (me, my brother &amp; sister (born 1942)) to Botley recreation ground. We saw a German plane shot down; it crashed in flames in a field before it could reach Oxford.In 1944, we had a visit from two American soldiers who were my grandmother's sister's sons. They came over for the Normandy landings. My Aunt Lucy came from Toronto on a liner (she had married a man from London and emigrated to Canada in the 1890s). They founded a successful electronics firm.I remember listening to the Home Service and hearing about the 8th Army advancing in Africa.<br>In 1944, we had the second lot of evacuees, this time from Surrey following a doodlebug attack. They stayed for a couple of months.<br>1945 - On VE Day we put a union jack flag on a broom and hung it outside the window.Rationing - my grandmother had a successful coal and wood business in Witney (Walter Bartlett &amp; Son), so she was quite well-off. She would bring us coal. My grandfather (O'Brien) worked at the Carlton Club in London at the start of the war. He became librarian and cashier there. It was bombed in 1940, so there's no record of his employment. His son, J.R.P. O'Brien became a don at Pembroke College. My uncle, C.F.R. Sibley was rescued by a destroyer at Dunkirk.

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: Other · Consensus signal: Other
Teacher disagreement score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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
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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