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Record W2110771524 · doi:10.5430/jms.v4n2p52

Electronic Books Impact Global Environment—An Empirical Study Focus on User Perspectives

2013· article· en· W2110771524 on OpenAlexvenueno aff
Chiang‐nan Chao, Leonora Fuxman, I. Hilmi Elifoglu

Bibliographic record

VenueJournal of Management and Strategy · 2013
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingReading (process)Electronic publishingFocus (optics)Empirical researchAdvertisingBusinessMarketingComputer scienceWorld Wide WebPolitical scienceArtLiterature

Abstract

fetched live from OpenAlex

This study examines the differences in behavior perspectives between the users of ebooks and printed books. The study focuses on a range of behavioral issues about ebook adoptions. These managerial issues will not only be strategic to the publishing industry and the paper industry’s bottom lines, but will also impact our future environment. The study finds the respondents spent slightly more time on reading printed books compare to ebooks. Digital books, however, have significant advantages in many aspects over the printed books. Although ebook adoption is a rapidly growing trend, it still lacks some of the advantages of the traditional printed books, e.g. there are many different and incompatible platforms for the usage of ebooks, and the consumers do not need to have the concern of a copyright for printed books. The findings of this preliminary study suggest that publishers may need to promote ebooks more aggressively and not only as a way to reduce the cost, but also as a way to preserve our global environment.

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)0.002
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.261
Teacher spread0.246 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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