From wampum to blockchain; from gold rush to “code rush” Indigenous currencies: leaving some for the rest in the digital age. Ashley Cordes, 2025, The MIT Press.
Bibliographic record
Abstract
Currencies tend to be associated with capitalism as an essential ingredient to capitalism’s operations. Cryptocurrencies, by extension, tend to be associated with right-wing libertarian ideals on the one hand and, more recently, windfall investments on the other. There are historical reasons for these associations—capitalism is currently the dominant mode of production, and money plays a significant role in its terms and conditions, whereas the promises of cryptocurrency revolutionizing exchange have given way to a hoarding tendency in which cryptocurrency is a vehicle for investment rather than a form of exchange. But while such conditions have helped to produce an amnesia surrounding the polyvalent character of currencies writ large, they have also obscured the Indigenous origins to which our collective social, political, and economic relations with currency in the present inherits, acknowledged or not. Such is the terrain upon which Ashley Cordes’ (Kō-Kwel/Coquille Nation) Indigenous Currencies: Leaving Some for the Rest in the Digital Age (The MIT Press, 2025) builds to tell a story of the many meanings and functions of Indigenous currencies primarily in what is now called the United States. Turning to Wampum Belts as an illustration of the very kinds of polyvalent dynamics present in cryptocurrencies today, Cordes approaches currencies as media and technology to build an account of Indigenous currencies situated along and against the long durée of capitalism and colonialism as mediated by currency. In form and function, Haudenosaunee Wampum Belts are “a ledger system composed of purple and white shells (binary expressions),” which “were strung (encoded) in arrangements that could be deciphered (decoded) to reveal transactional records” (p. 2). Cordes cautions that there isn’t the basis for a one-to-one comparison between wampum and cryptocurrencies since wampum’s money-function has historically been overemphasized, these dynamics parallel the binary code in computer programming and the kinds of encryption and decryption processes integral to cryptocurrencies in ways that are not merely metaphorical, abstracted bases of comparison.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".