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Record W4389967811 · doi:10.1017/9781788212267

Blockchain and the Digital Economy

2020· book· en· W4389967811 on OpenAlexaff
Fred Steinmetz, Lennart Ante, Ingo Fiedler

Bibliographic record

VenueAgenda Publishing eBooks · 2020
Typebook
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsConcordia University
Fundersnot available
KeywordsBlockchainDigital economyThe InternetCryptocurrencyBusiness modelDistributed ledgerBusinessEngineeringEconomyCommerceEconomicsComputer scienceComputer securityWorld Wide WebMarketing

Abstract

fetched live from OpenAlex

Blockchain technology has the potential to disrupt digital interaction across our economy and society. As the internet has changed our lives, the potential for blockchain and distributed ledger technologies to do the same is considerable. Fred Steinmetz, Lennart Ante and Ingo Fiedler assess this rapidly developing technology and its imminent economic and societal impact.<br><br>The ideas behind blockchain technology stem from an open-source movement and build on existing technology to facilitate the exchange of value in general and assets in particular via a protocol on top of the internet. Current platform-based business structures face the risk of being replaced by evolving decentralized ecosystems and individuals are set to become empowered by sovereignty over their digital data and footprints.<br><br>The book begins by explaining the key concepts of blockchain technology and presents an overview of the involved technical and economic elements. These form the basis for a discussion of the socio-economic implications of this new technology. This is followed by an in-depth analysis of significant case studies in the sectors of energy, digital identity, capital markets, logistics and gambling that outline the risks and benefits of the technology. The book strives to be non-technical and accessible, and to demystify the functionalities of blockchains and their potential for a range of readers in the wider social sciences.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.518
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0050.000
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.201
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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