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Record W7033374379

The Press Gallery #157: The This Is Why You Can't Have Nice Things edition

2016· other· en· W7033374379 on OpenAlexaboutno aff

Bibliographic record

VenueBulletin of Miscellaneous Information (Royal Gardens Kew) · 2016
Typeother
Languageen
FieldComputer Science
TopicAdvanced Computational Techniques in Science and Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsLegislatureNiceGovernment (linguistics)Legislative assemblyLegislative intent
DOInot available

Abstract

fetched live from OpenAlex

It's only the first week back for the fall sitting of the Alberta legislature, and we've already seen the government get into trouble in the legislature over its carbon tax ads, and an all-party committee dissolved because members couldn't play nicely with one another. That, Alberta politics, is why you can't have nice things.Join legislative reporter and host Emma Graney as she chats with Edmonton Journal columnists Graham Thomson and Paula Simons, and legislative reporter Stuart Thomson, about highlightLearn more about your ad choices. Visit megaphone.fm/adchoices

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.817
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0090.005
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.8170.753

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 head0.007
GPT teacher head0.201
Teacher spread0.195 · 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.

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
Published2016
Admission routes1
Has abstractyes

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