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Record W4385721777 · doi:10.59962/9780774850049-001

Preface

2007· book-chapter· en· W4385721777 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of British Columbia Press eBooks · 2007
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDiscrimination and Equality Law
Canadian institutionsnot available
FundersU.S. Department of Justice
KeywordsComputer science

Abstract

fetched live from OpenAlex

As a child, I grew up in a warm supportive environment with nine brothers and a sister.Our parents never dissuaded us from the notion that we could do anything we wanted to as long as we worked hard enough to accomplish it.It never would have crossed my mind that this applied only to my brothers.I was a true believer in formal equality in the paid workforce.However, I also held the view that women should have a choice as to whether they ventured out into the paid workforce or whether they married and stayed at home to work.I don't recall thinking that men had this same choice.As an articling student and lawyer called to the bar in British Columbia and Alberta, I never personally experienced discrimination until I worked for the federal Department of Justice in Vancouver in 1983.Members of the Canadian Bar Association Task Force on Gender Equality in the Legal Profession tittered at the unintentional irony of my statement to them in January 1993, ten years after my experience at the Department of Justice.I was no Perry Mason in court, but as an articling student with the Department of Justice I had heard by the grapevine that a Provincial Court Judge in Vancouver had commented that I had "good courtroom presence."Two years previous, while articling in Calgary, I conducted a trial against a lawyer who had been called to the bar for seven years.He demanded to know where else I was called and was loath to believe this was the first trial of my short career as a lawyer.I was blatantly discriminated against at the Department of Justice when I applied for a position in the Criminal Section to follow my call to the bar in British Columbia.I was up against a man who had told men lawyers in the office that he would "never hire a woman as a prosecutor, because women aren't any good in court."To my face he was much nicer.He told me that I had an impressive résumé and good reports from other lawyers on my work as an articling student.However, he did want someone to start

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)0.003
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 score0.372
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.3720.199

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 head0.060
GPT teacher head0.258
Teacher spread0.198 · 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
Published2007
Admission routes1
Has abstractyes

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