[Episode 0638] [The Great Reset] A Letter from a Viewer
Bibliographic record
Abstract
Ian reads over an email he received in response to the videos on China's social credit system and how it's affecting Canada.The video version of this episode is available here:https://unsafespace.com/ep0638About The Great ResetHosted by Ian K (a.k.a. Comix Division) \\"The Great Reset\\" is a series dedicated to understanding, preparing for, and opposing the World Economic Forum's initiative to radically undermine individual rights and permanently alter the economic and political landscape of the entire globe. Thanks for Watching!The best way to follow Unsafe Space, no matter which platforms ban us, is to visit:https://unsafespace.comWhile we're still allowed on YouTube, please don't forget to verify that you're subscribed, and to like and share this episode. You can find us there at:https://unsafespace.com/channelFor episode clips, visit:https://unsafespace.com/clipsOther video platforms on which our content can be found include:LBRY: https://lbry.tv/@unsafeBitChute: https://www.bitchute.com/channel/unsafespace/Also, come join our community of dangerous thinkers at the following social media sites...at least until we get banned:Censorship-averse platforms:Gab: @unsafeMinds: @unsafeLocals: unsafespace.locals.comParler: @unsafespaceTelegram Chat: https://t.me/joinchat/H4OUclXTz4xwF9EapZekPgCensorship-happy platforms:Twitter: @_unsafespaceFacebook: https://www.facebook.com/unsafepageInstagram: @_unsafespaceMeWe: https://mewe.com/p/unsafespaceSupport the content that you consume by visiting:https://unsafespace.com/donateFinally, don't forget to announce your status as a wrong-thinker with some Unsafe Space merch, available at:https://unsafespace.com/shop
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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.683 | 0.004 |
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".