Teens Against Empire: Gravel 2020 and the Anti-Imperialist Left
Bibliographic record
Abstract
An insurgent, largely social media-driven campaign is bringing a radically anti-imperialist and anti-war message to the 2020 American election discourse. But how did a trio of teenagers convince an 89 year-old former Alaska Senator to make a presidential run? On this episode, senior campaign staff Jonathan Suhr and Alex Chang join host Andre Goulet on an ambitious dual-cast of Korean history and current affairs show 'The Korea File' and Canadian left politics podcast 'Unpacking the News' to discuss the extraordinary anti-imperialist, anti-colonial campaign of Gravel 2020. Subscribe to the show on iTunes and Spotify and become a monthly patron at patreon.com/unpackingthenews To donate to the Gravel 2020 campaign go to https://secure.actblue.com/donate/mikegravel2020 Associate Production from Savanna Craig. This conversation was recorded on May 16th, 2019.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.200 | 0.008 |
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".