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Record W7061652187

Quesnel, Lowell oral history interview

2013· other· en· W7061652187 on OpenAlexaboutno aff

Bibliographic record

VenueSummit (Simon Fraser University) · 2013
Typeother
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsOral historyBrotherEstateChinatownBluesWork (physics)White (mutation)
DOInot available

Abstract

fetched live from OpenAlex

Lowell Quesnel was a barber who later became a real estate agent in New Westminster. He moved to New Westminster with his family from Williams Lake in 1946, when he was 13 years old. Quesnel attended St. Peter’s School (grade 6-9) and Duke of Connaught High School (located where New West city hall is now located). He dropped out in grade 12 and took a job at Pacific Veneer on the waterfront. He also worked as an “ice man” at the Queen’s Park arena, before buying his father’s barbershop at Joyce Road (now Joyce Street) at Kingsway in Vancouver for $800. He sold the barbershop approximately a year later (in 1955) for $1600. He then went to work for his father in Elks Barbershop on Carnavon St in New Westminster.1 One summer, when he was 15 or 16 years old, Quesnel and his brother worked at Pacific Coast Terminals (PCT) labelling salmon and stencilling boxes. Quesnel continued working as a barber until the emergence of The Beatles phenomenon of the 1960s, which led to the popularity of long hair for men. Business at the barbershop was so slow that Quesnel decided to work in real estate.

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)0.007
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: Other
Teacher disagreement score0.178
Threshold uncertainty score0.594

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.002
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.1780.019

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 head0.016
GPT teacher head0.189
Teacher spread0.173 · 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
Published2013
Admission routes1
Has abstractyes

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