Roy Green Show Podcast January 25: Remembering the Roy Green Show on its last weekend, Canada as we close in on the federal election, U.S. president Donald Trump may engage tariffs on Canada one week from today and the U.S./ Canada Border security issue
Bibliographic record
Abstract
Today's podcast:Remembering the Roy Green Show on it's last Saturday showGuests:Devon Peacock. Morning show host at AM 980, London, ONShaye Ganam. Midday host on CHQR, Calgary and CHED, EdmontonGreg Brady. Host. Toronto Today. AM 640, TorontoGreg Mackling, co-host of The Start Morning Show and Blue Bombers broadcast crew member at CJOB radio, Winnipeg.Canada as we close in on the federal election.Guest: Pierre Poilivre. Leader, Conservative Party of CanadaU.S. President Donald Trump may engage tariffs on Canada one week from today.Guest: Dr. Eric Kam. Macroeconomics professor. Toronto Metropolitan University.The U.S./Canada border security issue, which according to president Trump was the initial challenge for the U.S. and its dealings with us.Guest: Richard Kurland. Immigration lawyer, Vancouver. Advisor to the Quebec and federal governments on immigration matters---------------------------------------------Host/Content Producer - Roy GreenTechnical Producer - Leonardo CoelhoPodcast Producer - Jonathan ChungIf you enjoyed the podcast, tell a friend! For more of the Roy Green Show, subscribe to the podcast!https://globalnews.ca/roygreen/Learn more about your ad choices. Visit megaphone.fm/adchoices
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".