ELL 267: Retalk_ CEO, Clinton Scandals and Super Bowl Reax
Bibliographic record
Abstract
My guest today is Gordon Magill. He is a trucker with over 25 years on the road across 4 different countries. You can find him on twitter @ghostofgord. Gord has written articles for several outlets about the Freedom Convoy and is also plugged into what's happen on the ground in Ottawa. Invest in your future with iTrustCapital and get $100 of Bitcoin FREE with your first deposit! Listen to the best interviews from the Felony Friday and Finding Freedom archive, published every Tuesday in John's Finding Freedom Show solo feed. Listen and Subscribe on Apple Podcasts and Spotify. Get access to all of our bonus audio content, livestreams, behind-the-scenes segments and more for as little as $5 per month by joining the Lions of Liberty Pride on Patreon OR support us on Locals! Lions also get 20% off all merchandise at the Lions of Liberty Store, including our hot-off-the-press Hands Up Don't Nuke! T-Shirt!
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.516 | 0.354 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".