The Capital As Power Approach. An Invited-then-Rejected Interview with Shimshon Bichler and Jonathan Nitzan
Bibliographic record
Abstract
PREFACE. This interview was commissioned in October 2019 for a special issue on ‘Accumulation and Politics: Approaches and Concepts’ to be published by the Revue de la régulation. We submitted the text in March 2020, only to learn two months later that it won’t be published. The problem, we were informed, wasn’t the content, which everyone agreed was ‘highly interesting and stimulating’. It was the format. To begin with, the text was suddenly deemed ‘too long’. Although the length was agreed on beforehand, the special-issue editors — or maybe it was their bosses on the Editorial Board — now insisted that we cut it by no less than two-thirds. They also instructed us to make our answers more ‘interview-like’ and ‘personal’. Finally, and perhaps most tellingly, they demanded that we change our ‘tone’, which they found ‘unfair’ and ‘one-sided’. Translation: we should take a hike. This encounter with two-minded editors wasn’t our first. In another Review of Capital as Power paper, titled ‘Manuscripts Don’t Burn’, we sketch our history with Jekyll & Hyde editors who often used ‘length’ and ‘tone’ to reject articles they had invited but couldn’t stomach. But first, the original interview, in full.
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 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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".