How do you "follow the science" when there are contradictions? Why are people increasingly cranky & bothersome? Unpacking the ousting of Erin O'Toole & What's it like on the ground at the 2022 Winter Olympics?
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.
Bibliographic record
Abstract
How do you \\"follow the science\\" when there's science that contradicts other scientific findings?Guests: Dr. Victor Menaldo, Professor, Political Science, University of Washington Dr. Mark Smith, Professor, Political Science & Adjunct Professor, Comparative Religion and Communication-Being locked down and isolated from others can be bad enough on its own however when you start factoring burn out's impact on a person, it starts to make sense why they might be a little more aggressive and unpleasant to be around. How common is this state becoming and how bad does it appear to be?Guest: Dr. Richard Cytowic, Neurologist, Speaker & Author-Erin O'Toole has been booted from his role as leader of the Conservative Party of Canada. There's a lot to unpack here so Scott made sure to bring some help.Guest: Kate Harrison, Vice Chair, Summa Strategies-The Winter Olympics have a very peculiar history and as we near the opening ceremony for the 2022 Olympics, Scott gets a feel for what it's like in Beijing with help from someone who's there on the ground while also revisiting some of the Winter Games from years past.Guest: Philip Barker, Writer, insidethegames.biz & Executive Committee Member, International Society of Olympic Historians
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.184 | 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 it