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E-Commerce Background

2010· book-chapter· en· W2490354235 on OpenAlexaff
Mahmud Akhter Shareef, Yogesh K. Dwivedi, Michael D. Williams, Nitish Singh

Bibliographic record

VenueIGI Global eBooks · 2010
Typebook-chapter
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsCarleton University
Fundersnot available
KeywordsRestructuringThe InternetContext (archaeology)GlobalizationBusinessGovernment (linguistics)CivilizationCommerceE-commercePower (physics)TelecommunicationsEngineeringPolitical scienceEconomicsMarket economyComputer scienceWorld Wide WebFinanceGeography

Abstract

fetched live from OpenAlex

Electricity, telecommunication, integrated circuits, networking, and the Internet fostered a revolution by delivering distinctive services. The power of the net lies in restructuring the global economy to benefit consumers, businesses, society, and civilization. In general sense, electronic economy is considered to have three primary components--supporting infrastructure, electronic processes (how it is conducted), and electronic commerce transactions (buying and selling). This chapter deals with the introductory concepts of B2C EC and its related issues in the global context. It describes introduction to EC and evolution of one of the basic components of EC, that is, the Internet. Then this chapter sheds light on definition, proliferation, adoption, globalization, and management issues related to B2C EC. This chapter also explains the basic concepts of M-Commerce and E-Government (EG) as the related issues.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.104
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0020.001
Scholarly communication0.0070.006
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.1040.044

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 head0.034
GPT teacher head0.295
Teacher spread0.261 · 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 designNot applicable
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

Citations0
Published2010
Admission routes1
Has abstractyes

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