Creation and Resilience of Decentralized Brands - Bitcoin & the Blockchain
Bibliographic record
Abstract
This dissertation is based on a longitudinal ethnographic and netnographic study of the Bitcoin and broader Blockchain community. The data is drawn from 38 in-depth interviews and 200+ informal interviews, plus archival news media sources, netnography, and participant observation conducted in multiple cities: Toronto, Amsterdam, Berlin, Miami, New York, Prague, San Francisco, Cancun, Boston/Cambridge, and Tokyo. Participation at Bitcoin/Blockchain conferences included: Consensus Conference New York, North American Bitcoin Conference, Satoshi Roundtable Cancun, MIT Business of Blockchain, and Scaling Bitcoin Tokyo. The research fieldwork was conducted between 2014-2018. \nThe dissertation is structured as three papers: \n-\tSatoshi is Dead. Long Live Satoshi. The Curious Case of Bitcoin: This paper focuses on the myth of anonymity and how by remaining anonymous, Satoshi Nakamoto, was able to leave his creation open to widespread adoption. \n-\tTracing the United Nodes of Bitcoin: This paper examines the intersection of religiosity, technology, and money in the Bitcoin community. \n-\tOur Brand Is Crisis: Creation and Resilience of Decentralized Brands Bitcoin & the Blockchain: Drawing on ecological resilience framework as a conceptual metaphor this paper maps how various stabilizing and destabilizing forces in the Bitcoin ecosystem helped in the evolution of a decentralized brand and promulgated more mainstreaming of the Bitcoin brand.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".