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Record W6981966354

Gilbert Paul Jordan - The Boozing Barber (BC)

2018· other· en· W6981966354 on OpenAlexaboutno aff

Bibliographic record

VenueBulletin of Miscellaneous Information (Royal Gardens Kew) · 2018
Typeother
Languageen
FieldArts and Humanities
TopicAcademic Writing and Publishing
Canadian institutionsnot available
Fundersnot available
KeywordsLuckIndigenousPretextContent (measure theory)CriticismSubject (documents)
DOInot available

Abstract

fetched live from OpenAlex

Episode 015: Gilbert Paul Jordan, a.k.a. the Boozing Barber, loved to drink and carouse with women. Between 1965 and 1987 nine women died of acute alcohol poisoning while in his company, while four others came close. Although police knew about Jordan's involvement in theier deaths. his prey were the down on their luck indigenous women from Vancouver's downtown East Side. Cops tended believed him when he told them he found them that way, even though he was a violent and sadistic career criminal. Women like that die every day down town. He was only ever charged with one murder and how that turned out was shocking too. CONTENT WARNING: Dark Poutine is not for the faint of heart or squeamish. Our content contains mature themes, coarse language and may include graphic descriptions of violent crimes. Listener discretion is strongly advisedWeb: http://darkpoutine.com/ Facebook: https://www.facebook.com/darkpoutine/ Twitter: https://twitter.com/darkpoutinepod Instagram: https://www.instagram.com/darkpoutine/ Email: darkpoutinepodcast@gmail.com Writer / Creator, Researcher & Host: Mike Browne (@mikebrowne) Audio Production, Original Music & Cohost: Scott Hemenway (@sdhpics) Intro: LeftBehind Podcast Support the show: https://www.patreon.com/darkpoutine See omnystudio.com/policies/listener for privacy information.

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 categoriesInsufficient 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 score0.702
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.7270.025

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 head0.010
GPT teacher head0.183
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; 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
Published2018
Admission routes1
Has abstractyes

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