Bibliographic record
Abstract
Abstract In this article, I discuss the role of crypto‐coins (CCs) as an explicit response to the 2008 economic crisis. Drawing on networked social movement theory, I outline the tension between blockchain based currencies promoted as horizontal markets of technological innovation and the persistence of hierarchical authority within these networks. By examining the assumptions about authority and regulation that underpin the creation of CCs I argue that the cryptographic responses to the financial crisis understate the persistence of hierarchies in their claims to upset traditional monetary authority. As global networks, blockchain technology faces political challenges from regulators, hegemonic market actors and adherents devoted to particular CCs. Consequently, I draw parallels between CCs and networked social movements to argue that the emergence and proliferation of ‘altcoins’ presents a series of political challenges to the framing of CCs as disruptive technology. The innovative character of CCs relies on tacit and explicit assumptions about politics that social movement theory can help to explain. By examining the altcoins through the concepts of resource mobilization, framing and identity formation, I argue that the dynamics of offline social hierarchies persist and are magnified in the world of CCs.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".