Episode 126: Trans Fats, the Pay Gap, Passwords, Physiospect
Bibliographic record
Abstract
On this episode of Life, the Universe & Everything Else, Gem, Lauren, Laura, and Ashlyn take listener requests! The panel discusses trans fats, the gender pay gap, password security, and an exciting form of health pseudoscience called the physiospect.Life, the Universe & Everything Else is a podcast that delves into issues of science, critical thinking, and secular humanism.SkeptiCamp Talks:SkeptiCamp Winnipeg 2017Pysiospect:Skeptophilia: Quantum frequency box | Physiospect diagnostics & therapy bioresonance unitTrans Fats:Minister Petitpas Taylor announces Government of Canada ban on partially hydrogenated oils (Government of Canada) | Prohibiting the Use of Partially Hydrogenated Oils (PHOs) in Foods (Dietitians of Canada) | Prohibiting the Use of Partially Hydrogenated Oils (PHOs) in Foods (Government of Canada Notice of Modification) | Trans fat (Wikipedia) | Fat and Why it Matters (Indiana University) | Ruminant trans fatty acids and coronary heart disease—cause for concern? (International Journal of Epidemiology)Password Security:Changes in Password Best Practices (Schneier on Security) | Digital Identity Guidelines: Authentication and Lifecycle Management (NIST) | Best practices for passwords updated after original author regrets his advice (The Verge)Gender Pay Gap:Earnings (Bureau of Labor Statistics) | Gender wage gap data (OECD) | The Simple Truth about the Gender Pay Gap (AAUW) | Science faculty's subtle gender biases favor male students (PNAS) | The Actual Science of James Damore's Google Memo (Wired) | Why Men Don't Believe the Data on Gender Bias in Science (Wired)Contact Us:Facebook | Twitter | EmailListen:Direct Link | Apple Podcasts | Google Play | Stitcher | RSS Feedhttp://media.blubrry.com/luee/winnipegskeptics.files.wordpress.com/2017/11/126-listener-requests.mp3
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.349 | 0.006 |
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; both teacher heads 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".