Digital Dispersion: An Industrial and Geographic Census of Commerical Internet Use
Bibliographic record
Abstract
Our study provides the first census of the dispersion of Internet technology to commercial establishments in the United States.We distinguish between participation, that is, use of the Internet because it is necessary for all business (e.g., email and browsing) and enhancement, that is, adoption of Internet technology to enhance computing processes for competitive advantage (e.g., electronic commerce).Employing the Harte Hanks Market Intelligence Survey, we examine adoption of the Internet at 86,879 commercial establishments with 100 or more employees at the end of 2000.Using routine statistical methods, we focus on answering questions about economy-wide outcomes: Which industries had the highest and lowest rates of participation and enhancement?Which cities, states and industries had a typical experience and which did not?We arrive at three conclusions.First, participation and enhancement display contrasting patterns of dispersion.In a majority of industries participation has approached saturation levels, while enhancement occurs at lower rates and with dispersion reflecting long standing industrial differences in use of computing.Second, the creation and use of the Internet does not eliminate the importance of geography.Leading areas are widespread, whereas laggards are more common in smaller urban areas and some rural areas.However, the distribution of industries across geographic regions explains much of the difference in rates of adoption of the Internet in different areas.Third, commercial Internet use is quite dispersed, more so than previous studies show.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".