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Record W6982249590

Hughan, Helen oral history interviews

2014· other· en· W6982249590 on OpenAlexaboutno aff

Bibliographic record

VenueSummit (Simon Fraser University) · 2014
Typeother
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsOral historyGeorge (robot)ParliamentPlan (archaeology)Government (linguistics)Work (physics)
DOInot available

Abstract

fetched live from OpenAlex

Helen Hughan is a resident of New Westminster who worked as a secretary for Mercer Shipyards in New Westminster from 1944 to 1949. Her parents immigrated to New Westminster from Scotland in 1929 when she was three years old. She lived on the corner of Mowat St. and 3rd Avenue, and her father worked at Mohawk Lumber Company. She went to F.W. Howie Elementary School and Trapp Tech High School. In 1944, when she was senior in high school, she left school early because there was a demand for secretaries. During that time businesses would go to high schools to look for secretaries. Her major duties as a secretary were recording timesheets from the employees, typing letters, ordering supplies, and interacting with office visitors. Starting out at 18 years old, she believes that working at Mercers opened up to a lot of new things and experiences that she was never exposed to, living a sheltered life as an only child. Hughan reports that she was very happy working at Mercers because her job was challenging and exciting. She married in 1949 and moved to Burnaby, where her children were raised, and moving back to New Westminster in 1971. Prior to her secretary position, in high school she worked at Copp’s Shoe Store on Columbia St. on Saturdays. Throughout the interview she tells stories about the Mercers (father Ed, and sons Gordon, and Art (Arthur)) who she worked with, and speaks enthusiastically about her experience working for them.

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)0.003
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.064
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0210.007
Scholarly communication0.0080.007
Open science0.0020.006
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0640.006

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 head0.012
GPT teacher head0.180
Teacher spread0.168 · 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
Published2014
Admission routes1
Has abstractyes

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