Grand strategies of the left: the foreign policy of progressive worldmaking
Bibliographic record
Abstract
As Samuel Huntington predicted over 40 years ago, ours is a time of ‘creedal passion’. The post-Cold War United States foreign policy consensus has eroded, and the ideational scope of American political practice has expanded on both the far right and the far left. Since 2016, scholars have tried to decipher the meaning of ‘Trumpism’ and ‘America First’, but similar efforts have not been made with respect to left-wing foreign policy. Specific left-wing issues get noticed—like the questions of Palestinian statehood and global inequality—but, with few exceptions, a unified theory of left-wing grand strategy has not been put forward. Van Jackson's ambitious Grand strategies of the left sets out to remedy this oversight with a deeply informed and policy-relevant account of left-wing international thought. Jackson is well suited for the task. As an academic, his specialization in east Asian security gives him the scholarly authority to speak on these issues at a high level of abstraction, while his immersion in the left-wing ‘Twitterverse’, the podcast world and the think tank scene brings him into contact with the latest currents.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".