Categorical Anarchy in the UK? The British Media’s Classification of Bitcoin and the Limits of Categorization
Bibliographic record
Abstract
Bitcoin is difficult to categorize and indeed has been associated with 112 different labels in the British media (e.g., “private money,” “commodity”) – most of which poorly describe bitcoin. Specifically, our analyses of 674 media articles, focusing on the relationship between labeling and categorization, identify classification inconsistencies at three levels: within clusters of labels, between labels and categories, and between category attributes. These inconsistencies hamper categorization based on attribute similarity, audience goals, and causal models, respectively. We identify four factors that nurture this categorical anarchy and conclude with a call for research on the socioeconomic revolution heralded by blockchain 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".