New World Next Week: Episode125 - Iceland Was Right, Terror Futures, West Nile Spraying
Bibliographic record
Abstract
NewWorldNextWeek.com : Episode125 - Iceland Was Right, Terror Futures, West Nile Spraying Welcome to New World Next Week - the video series from Corbett Report and Media Monarchy that covers some of the most important developments in open source intelligence news. This week: Story #1: Iceland Was Right, We Were Wrong: IMF Rothschild, Paulson, Soros All Betting Financial Disaster Coming For Euro Shares, Euro fall as Greek meetings loom Skilled Work - Without The Workers Story #2: US Intelligence Tests Crowd-Sourcing Against Its Experts Hackers Backdoor The Human Brain, Successfully Extract Sensitive Data Goldman Sachs To Blame For Global Food-Oil Price Crisis Evidence For Informed Trading On 9/11 Attacks Sentient World Simulation: Meet Your DoD Clone Story #3: CDC Claims Over 1,100 West Nile Virus Cases In US Cities, Counties Nationwide Begin Mass Aerial Sprayings Of Toxic 'Anti-West Nile Virus' Pesticides Pilots Told To Avoid George Bush's Home During Aerial Spraying Opponents To Launch Signature-Gathering Initiative To Block Fluoridated Water In Portland Visit NewWorldNextWeek.com to get previous episodes in various formats to download, burn and share. We also have weekly episodes from FoodWorldOrder.com . And as always, stay up-to-date by subscribing to the feeds from Corbett Report here and Media Monarchy here . Thank you. Previous: Episode124 - USrael War Drums, TrapWire Octopus, Canadian Climate Con
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.552 | 0.322 |
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 source (direct Gemma or distilled Codex), 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".