Potential and actual socio-economic impacts of blockchain
Bibliographic record
Abstract
Data in qualitative and quantitative forms serve as a necessary foundation for social, political and economic life. Data can only unfold its true power if it is structured and put into a context to provide users with useful information for decision-making processes. By giving order to data, ledgers like blockchains, centralized databases or analogue registers allow ownership to be tracked – a function that is fundamental to the idea of property and modern society. Ledgers provide a record that coincides with the collective belief of a current state of affairs among members in a society. By introducing the idea of credit, ledgers became even more powerful by tracking who owes what to whom. Essentially, ledgers provide evidence for any change in the status quo and can be referred to during any dispute. Ledgers are fundamental to the organization of human affairs (Berg et al . 2018). The oldest forms of ledgers date back to Babylonian temples. Ledger technologies have constantly evolved with double-entry bookkeeping and digital databases being the most notable breakthroughs of the modern era. Without them, modern economic and social infrastructure such as complex corporations, markets, administrations and governments could not exist. Not only do all organizations and institutions rely on storing and updating data in trustworthy ledgers, from the registration of births and deaths to home ownership, mortgages, credit cards and mobile phone contracts, our entire modern society is based on records and ledgers. The maintenance of today's ledgers is regularly provided by the state and large corporations and all share one important characteristic: centralization. There is one master ledger or database that is considered to hold the absolute truth. This provides enormous efficiency, since updates to the ledger only need to be conducted once in the central ledger by just one bookkeeper. At the same time, this bestows the central bookkeeper with enormous power, and it took centuries to develop political systems of checks and balances that prevent the wildest forms of misuse of this power. But even in modern democracies, the risk of exclusion is a constant threat to minorities. Apart from the risks that come with such power of central authority, centralized ledgers are inherently fragile, and the record can still be lost.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".