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Record W29378538 · doi:10.3233/iwa-2003-00028

Charting and Bridging Digital Divides

2003· article· en· W29378538 on OpenAlexaff
Wenhong Chen, Barry Wellman

Bibliographic record

VenueI-WAYS Digest of Electronic Commerce Policy and Regulation · 2003
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDigital divideThe InternetGlobeBridging (networking)Internet accessInternet privacyPolitical scienceComputer scienceWorld Wide WebComputer securityPsychology

Abstract

fetched live from OpenAlex

Many assume that the digital divide – the large numbers of people who are not connected to the Internet – is small, shrinking, and rapidly becoming irrelevant. It is not. The term “digital divide” refers to inequalities in Internet access and use, ranging from the global level, to nation states, to communities, and to individuals. The divide is here for some time to come. It is large, multifaceted, and, in some ways, it is not shrinking. Moreover, the divide is socially patterned, so that there are systematic and meaningful variations in the kinds of people who are on and off the Internet. These patterns vary between nations and over time, so last year’s divide often does not necessarily resemble this year’s, and Country A’s divide does not necessarily resemble Country B’s. Indeed, it is more accurate to use the plural – digital divides – because the nature of the digital divide varies within and between countries, both developed and developing. There is no one digital divide; there are many divides. To be sure, the Internet has grown rapidly and hugely in the last decade. Educated estimates show that use of the Internet has diffused to the point that the number of Internet users around the globe has surged from 900,000 in 1993, 25 million in 1995, 83 million in 1999, 513 million in 2001, to more than 600 million by the end of 2002. More recently, other new media, such as Web-enabled mobile phones, have fostered computermediated technology. Yet, widespread diffusion does not equal ubiquity, even within developed countries. The first digital divide appeared at the very start of the Internet. Early users were disproportionately affluent, male, white, better educated, and from developed countries, especially the United States. Rather than shrinking with expanding Internet use, the global digital divide between developed countries and developing nations continues to be huge. Denizens of economically developed countries sometimes forget what a small percentage of the world’s population is online. After all, the majority of their country-mates are on the Internet, as are the economically advanced segments of developing countries. Yet, only 10 percent of the world’s population was on the Internet in 2002, and 88 percent of these Internet users resided in industrialized countries. Within countries, the uneven diffusion of the Internet appears along familiar lines of social inequality such as socioeconomic status, gender, age, geographic location, and ethnicity. Moreover, having access to computers and the Internet and possessing the ability to use them effectively are two different issues. However, marketers, media, and governments often report only the number of people who have access to the Internet. The question is not whether people have ever glanced at a monitor or put their hands on a keyboard, but the extent to which they regularly use a computer and the Internet for meaningful purposes. At present, the digital divide has multiple aspects. As noted above, it is really digital divides. First, the digital divide is not a binary yes/no question of whether the basic physical access to the Internet is available. Access does not equal use. Rather, the digital divide is a continuum ranging from physical access, financial access, cognitive access, and content access to political access. Second, the term “digital divide” has both technological and social resonances. There are at least five

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations18
Published2003
Admission routes1
Has abstractyes

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