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
Slow Down is the English translation of the Japanese best-seller Capital in the Anthropocene, which sold over 500,000 copies during the pandemic in Japan alone, and turned Saito into what The New Yorker calls "a rare academic celebrity."The book builds on and expands previous arguments Saito made in his dissertation, published in 2017 as Karl Marx's Ecosocialism.Capital, Nature and The Unfinished Critique of Political Economy (Monthly Review Press) and his 2023 book Marx in the Anthropocene.Towards the Idea of Degrowth Communism (Cambridge University Press).In accessible and nontechnical language that will perhaps appeal more to readers unfamiliar with red and green theory than to experts, Saito makes a strong case for the contemporary relevance of what he claims was the later Marx's vision of "degrowth communism."The overall goal of Saito's project is to combine these two terms into a coherent vision: communism aims to shut down the systemic imperative of capitalism to externalize burdens onto nature and future generations, while degrowth commits communism to the goal of sustainable living within planetary boundaries.Degrowth is necessary because, as the Soviet Union shows (p.121), a productivist communism by itself could still aim at overall growth in productivity and economic progress, failing to recognize ecological limits.Chapter 4, "Marx in the Anthropocene" summarizes the core thesis of Saito's homonymous 2023 book.The argument is that,
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) | 0.000 | 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 it