How Do Industry Features Influence the Role of Location on Internet Adoption?
Why this work is in the frame
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Bibliographic record
Abstract
We provide a framework and evidence to confront two questions: Does the location of an establishment shape its adoption of different complex Internet applications even when controlling for an industry’s features? If location does matter, what features in an industry shape whether Internet adoption follows a pattern consistent with the urban leadership or global village hypotheses? Our findings show that both industry and location play a significant role in explaining the geographic variance in adoption. We also find that industries differ in their sensitivity to location. Information technology–using industries are more sensitive than are information technology–producing industries to the changes in costs and gross benefits affiliated with changes in location size. Moreover, industries with high labor costs and those that are geographically concentrated are more sensitive to changes in gross benefits that occur with increases in location size. Overall, our results provide evidence for an industrial digital divide.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it