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Record W6996588083

'Shark Tank' star to run for Conservative leadership in Canada

2017· other· en· W6996588083 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternet Archive (Internet Archive) · 2017
Typeother
Languageen
FieldSocial Sciences
TopicPolitical Developments and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsPrime ministerOpposition (politics)ThatcherismConservative government
DOInot available

Abstract

fetched live from OpenAlex

"Shark Tank" cast member Kevin O'Leary announced Wednesday he is running for the leadership of Canada's opposition Conservative Party. Canada is getting its own reality show politician.Kevin O'Leary, who is a cast member on the American show "Shark Tank," says he's running for the leadership of Canada's opposition Conservative Party.O'Leary is is a businessman and television commentator who has drawn comparisons to U.S. President-elect Donald Trump.Mark Burnett, who created "The Apprentice" with Trump, also produced "Shark Tank."On the show, entrepreneurs try to convince a cast of tycoons to invest in their ideas.O'Leary is best known in Canada as a former bombastic judge on CBC's "Dragons' Den," a Canadian equivalent of "Shark Tank."He says the party needs a candidate who can beat Liberal Prime Minister Justin Trudeau and bring jobs back to Canada. ...and he says he can handle Trump better than Trudeau can.O'Leary is competing against 13 lower-profile candidates.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.089
GPT teacher head0.303
Teacher spread0.213 · 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