Financialization x Gamblification
Bibliographic record
Abstract
The blurring of gambling and crypto-finance reflects a wider set of complex social transformations. To help parse these transformations, we discuss two key concepts: financialization and gamblification. On their own, these concepts are useful—if insufficient—for the critical theorization of cryptocurrency exchanges. Taken together, they help highlight the deep interrelationship of cryptocurrency exchanges and gambling in our contemporary moment. Reflecting on the example of BitMEX, a centralized cryptocurrency exchange notable for its gamified interface, we argue that cryptocurrency discourse may operate to obscure the structural mechanisms that transfer wealth from users to platform operators while further embedding speculative risk-taking deep within everyday life. Our article first notes some of the resonances in the ways that cryptocurrency exchanges and gambling markets are organized. We also indicate that cryptocurrency exchange—like gambling—draws some of its appeal from a backdrop of uncertainty and vast inequity in contemporary capitalism. Then, taking advantage of the ‘analytic multiplier effects’ that come from holding the concepts of financialization and gamblification together, we work to decrypt some of the obfuscating elements of cryptocurrency discourse.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.040 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".