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Record W7005068192

Podcast - Downtown development proposals, State of the Union speech, Iowa caucus & confusion in Ontario courtrooms

2020· other· en· W7005068192 on OpenAlexaboutno aff

Bibliographic record

VenueInternet Archive (Internet Archive) · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCaucusDowntownState (computer science)AppealLegislatureConfusionLegislation
DOInot available

Abstract

fetched live from OpenAlex

A third proposal for downtown venue options has been brought forward to council, this time by Pearle Hospitality.Guest: John Best, Publisher of the Bay Observer-How did the President do on his State of the Union speech last night? And the latest results for the Iowa Caucus show that Pete Buttigieg is holding a small lead over the other candidates, with Bernie Sanders close behind.Guest: Jack Colwell is a distinguished visiting journalist with the Gallivan Program in Journalism, Ethics, and Democracy at the University of Notre Dame-Is there chaos coming to Ontario court rooms? An Ontario Court of Appeal decision that found a legislative change in the law was incorrectly applied by Ontario judges in a dozen cases since September.Guest: Jeff Manishin. Criminal Lawyer, Ross & McBride/ Former Crown Attorney

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)0.001
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 score0.209
Threshold uncertainty score0.699

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.001
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2090.025

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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
Published2020
Admission routes1
Has abstractyes

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