Research Note—Social Interactions and the “Digital Divide”: Explaining Variations in Internet Use
Bibliographic record
Abstract
Given the increasingly important role of the Internet in education, healthcare, and other essential services, it is important that we develop an understanding of the “digital divide.” Despite the widespread diffusion of the Web and related technologies, pockets remain where the Internet is used sparingly, if at all. There are large geographic variations, as well as variations across ethnic and racial lines. Prior research suggests that individual, household, and regional differences are responsible for this disparity. We argue for an alternative explanation: Individual choice is subject to social influence (“peer effects”) that emanates from geographic proximity; this influence is the cause of the excess variation. We test this assertion with empirical analysis of a data set compiled from a number of sources. We find, first, that widespread Internet use among people who live in proximity has a direct effect on an individual's propensity to go online. Using data on residential segregation, we test the proposition that the Internet usage patterns of people who live in more ethnically isolated regions will more closely resemble usage patterns of their ethnic group. Finally, we examine the moderating impact of housing density and directly measured social interactions on the relationship between Internet use and peer effects. Results are consistent across analyses and provide strong evidence of peer effects, suggesting that individual Internet use is influenced by local patterns of usage. Implications for public policy and the diffusion of the Internet are discussed.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".