How to Steal a YouTube Rewind Video and Get Away With It | #BigThanks
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
Original Description: #YouTubeRemind: https://youtu.be/JOgprkHCyro No need to search for this douchebag. Damage is done. Time to move on. Hopefully he learned his lesson, cause 9 Year Old army will be watching him sleep at night. Thank you all for the support! 👊 SHOUT TO r/PewDiePieSubmissions r/Defranco r/YouTube Redditors who helped out in the fight! ❤️ Big Thanks to the maker of this video: http://www.youtube.com/subscription_center?add_user=PieDiePie FOLLOW CHRISAWAKE: Facebook: https://www.facebook.com/chrisawakemusic Twitter: https://www.twitter.com/chrisawakemusic Instagram: https://www.instagram.com/chrisawakemusic PATREON: https://www.Patreon.com/ChrisAwake WEBSITE: http://chrisawake.com ENJOY MUSIC by CHRISAWAKE: Soundcloud: https://www.soundcloud.com/chrisawake Bandcamp: https://www.chrisawake.bandcamp.com iTunes: https://itunes.apple.com/us/album/bedtime-stories/id978107404 Spotify: https://open.spotify.com/album/03ag2NgNhsEGa7FfauHuCU Why am I monetizing this video? I want that single quarter of a nickel from that guy. Or at least an extra mouse click. Karma is a B!tch Lasagna #YouTubeRewind #DramaAlert #PewDiePie
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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.094 | 0.005 |
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