Bibliographic record
Abstract
* Winner of the 2018 RECASP Essay Prize * \n \nAccording to Jonathan Nitzan and Shimshon Bichler (2009), capital is not an economic quantity, but a mode of power. Their fundamental thesis could be summarized as follows: capital is power quantified in monetary terms. But what do we do when we quantify? What is the nature of money in a capitalist society? Indeed, what is power? In the following, we try to develop a concept of power as the ability of persons to create particular formations against resistance. The kinds of formations persons can think of depend on the society they live in, which can be identified by what Cornelius Castoriadis called its social imaginary significations (SIS). The core SIS of capitalism is rational mastery operating with computational rationality. Computational rationality in turn rests on a particular understanding of how signification works: it works through operational symbolism, as theorized by Sybille Krämer in analyzing the philosophy of Leibniz. When the concept of the SIS of modern rationality was developed in the 1950s and 1960s, bureaucracy was seen as the main organizational mode of rational mastery. We argue that there are two modes of rational mastery, capitalization and bureaucratization, that interact with each other in capitalist society. The paper concludes with deliberations on the future of rational mastery and possible ways out. \n \n--- \n \nFRONT PICTURE: International Space Station Expedition 26 Crew (24 Dec 2010), Montreal at Night. Astronaut photograph ISS026-E-12474 (https://earthobservatory.nasa.gov/images/48471/montreal-at-night). Image courtesy of the Earth Science and Remote Sensing Unit, NASA Johnson Space Center (https://eol.jsc.nasa.gov/) \n \n--- \n \nBIO: The author studied physics and informatics, along with a lot of philosophy, but is also interested in many other subjects. He came across Bichler and Nitzan’s Capital as Power in the first decade of the twenty-first century when he was politically active in various ways. He rediscovered Castoriadis through one of Bichler and Nitzans’s works. Since then, he has tried to understand what Bichler and Nitzan actually mean by power. As there is no concrete answer to this question, he has been trying to develop one by (con)fusing concepts developed by Castoriadis and other thinkers with some of his own.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 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".