Roy Green Show Podcast (w/Guest Host: Rick Zamperin), Feb, 19: Phil Gurski, Pres. Borealis Threat and Risk Consulting, China and 2021 Fed Election. - Franco Terrazzano, Fed. Dir. Canadian Taxpayers Federation, Who in FedGov Stayed at $6k Hotel? - & More!
Bibliographic record
Abstract
Today's podcast: Top secret documents at CSIS reveal China's sophisticated strategy to influence the 2021 federal election. Some members of Parliament say they have no idea how to spot foreign interference, and they're asking Canada's spy agency for practical advice.GUEST: Phil Gurski, President of Borealis Threat and Risk Consulting, Distinguished Fellow with the University of Ottawa's National Security program, and former CSIS analyst.The Canadian Taxpayers Federation is seeking legal action against the federal government to reveal who stayed at a $6,000 a night hotel in London, England for Queen Elizabeth II's state funeral.GUEST: Franco Terrazzano, Federal Director, Canadian Taxpayers Federation.98-year-old Jimmy Carter, the 39th president of the United States, is now receiving hospice care at his home in Plains, Georgia.GUEST: Arthur Milnes, Fellow of the Queen's University School of Policy Studies, former speechwriter to Prime Minister Stephen Harper, and the author of several books, including those about Prime Minister John Turner, and U.S. Presidents George H.W. Bush, Franklin Roosevelt, and Jimmy Carter.New research shows Canadians have reached a tipping point when it comes to gratuities, and most would scrap tipping for higher service wages.GUEST: Dave Korzinski, Research Director, Angus Reid Institute.---------------------------------------------Host/Content Producer - Rick ZamperinTechnical/Podcast Producer - Tom McKayPodcast Co-Producer - Matt TaylorIf 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
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.044 | 0.001 |
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".