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Record W3080524163 · doi:10.33621/jdsr.v2i2.34

‘Blockchain Good, Bitcoin Bad’: The Social Construction of Blockchain in Mainstream and Specialized Media

2020· article· en· W3080524163 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Digital Social Research · 2020
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBlockchainFraming (construction)PopularityMainstreamSocial mediaSkepticismBig dataRhetorical questionPhilosophy of technologyCryptocurrencySociologyInternet privacyPolitical scienceEpistemologyComputer securityPhilosophy of scienceComputer scienceLawEngineering

Abstract

fetched live from OpenAlex

Blockchain is one of the most widely debated technologies in recent years. Pundits and scholars have described it as a disruptive technology that will impact many sectors of society. Skeptics argue blockchain’s popularity is fuelled by the media’s obsession for the ‘next big thing’ rather than the intrinsic potential of the technology. In this paper, we follow a social constructivist approach with the aim of explaining how different discourses are creating new meanings about this technology. As Communication scholars, we focus on the role media play in framing debates about blockchain. Our analysis relies on a human coding of the most popular news about blockchain circulating on Twitter from October 2014 to July 2018. The findings show the general attitude about blockchain is predominantly positive. The discourses developing around crypto technologies are complex and multifaceted and indicate a general transition in the rhetorical definition of blockchain.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.046
GPT teacher head0.325
Teacher spread0.279 · 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