Bibliographic record
Abstract
This article describes the emergence of a novel form of capital—which I call “cosine capital”—that finds objectified form in the “embedding” structures of large language models. In the past decade, massive neural network architectures transformed computational approaches to language in the form of large language models. This approach to modeling language is now being adapted to nearly any sequential data structure imaginable in both academia and industry. While these technologies have been hailed as revolutionary, I situate them within a continuous technological and philosophical lineage that runs directly back to the origins of cybernetics and information science, in particular Claude Shannon’s noisy channel model of communication. I imagine this noisy channel as a sort of “diagram of power,” arguing that a similar process of “enclosure” that commodified the bit as the foundational unit of information is now taking place with embeddings, objectifying them as fungible commodities across an increasing range of societal domains. I compare this cosine capital to Fourcade and Healy’s recent notion of “eigencapital,” suggesting that the particular technical features of embeddings—specifically, their inherently relational nature—challenge the eigencapital model and instead represent a fundamentally novel form of abstraction with strong implications for the future of capitalism and technology.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".