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Record W7027126813

'BradCast' 3/22/2017: (Dark money, 'Citizens United', Neil Gorsuch and Sheldon Whitehouse)

2017· other· en· W7027126813 on OpenAlexaboutno aff

Bibliographic record

VenueInternet Archive (Internet Archive) · 2017
Typeother
Languageen
FieldArts and Humanities
TopicCultural Studies and Interdisciplinary Research
Canadian institutionsnot available
Fundersnot available
KeywordsSupreme courtEconomic JusticeHigh CourtQuarter (Canadian coin)DissentLegal case
DOInot available

Abstract

fetched live from OpenAlex

Connecting the dots between the 'Citizens United' ruling, the GOP theft of the U.S. Supreme Court and the undermining of climate change legislation. On today's show, we go back to 1991, when a newly formed rightwing non-profit group by the name of Citizens United spent $100,000 to ensure that alleged sexual harasser Clarence Thomas would be confirmed as a Justice to the U.S. Supreme Court. Twenty years later, in 2010's 'Citizens United v. FEC' ruling, Thomas then helped unleash a tsunami of undisclosed 'dark money' into our political, electoral and judicial system. In 2014, Sen. Sheldon Whitehouse (D-RI) cited that fateful decision as the point in time when Republicans, who used to support action on climate change, immediately stopped doing so. And, this week, at the confirmation hearings for Judge Neil Gorsuch -- Donald Trump's nominee for the GOP's stolen Supreme Court seat -- Whitehouse confronted Gorsuch in a fascinating extended exchange about 'dark money' in politics, including the $10 million spent by undisclosed rightwingers backing his confirmation, and the $7 million those same shady groups spent to block the confirmation of Barack Obama's nominee, Judge Merrick Garland. All of that and more on today's program, including Desi Doyen and the latest 'Green News Report,' and the Colorado GOP's former chairman-turned-talk show host who has now been charged with absentee voter fraud...

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient 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 score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0010.000
Open science0.0020.004
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0250.002

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 head0.044
GPT teacher head0.277
Teacher spread0.233 · 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
Published2017
Admission routes1
Has abstractyes

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