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Record W3197123010

Hype around Bitcoin: Awareness and Prospective in India

2019· article· en· W3197123010 on OpenAlexaboutno aff
Shikha Agarwal, Rakhi Arora

Bibliographic record

VenueInternational Journal of Management, IT, and Engineering · 2019
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual currencyDigital currencyCurrencyGovernment (linguistics)CryptocurrencyBusinessLegal tenderOrder (exchange)CommerceEconomicsComputer securityFinanceMonetary economicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

After demonetization, the emphasis was given on cashless economy by the Government of India. Keeping in view the concept of cashless economy, importance of Crypto currency can not be denied. Crypto currency (CC) is a virtual currency and it works as a medium of exchange by using cryptography for security. It comprises diverse currencies such as Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC), Cardano (ADA), Neo (NEO), Stellar Lumens (XLM) and so on. Many countries like Canada, Australia, Bulgaria, Chile, Denmark, Estonia, Finland, Germany and Luxembourg have adopted Bitcoin in order to moving towards a digital eco-system. The research was conducted to find out the awareness, perception and understanding about the functioning of bitcoin among individuals. This paper is all about awareness of bitcoin amongst Individuals and prospective if allowed by the Government of India.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.005
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.221
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2019
Admission routes1
Has abstractyes

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