Capitalizing on the Internet: Social Contact, Civic Engagement, and Sense of Community
Bibliographic record
Abstract
How does the Internet affect social capital in terms of social contact, civic engagement, and a sense of community? Does online involvement increase, decrease, or supplement the ways in which people engage? Our evidence comes from a 1998 survey of North American visitors to the National Geographic Society website, one of the first large-scale web surveys of the general public. We find that online social contact supplements the frequency of face-to-face and telephone contact. Online activity also supplements participation in voluntary organizations and politics. Frequent email users have a greater sense of online community, although their overall sense of community is similar to that of infrequent email users. The evidence suggests that as the Internet is incorporated into the routine practices of everyday life, social capital is becoming augmented and more geographically dispersed. Acknowledgments This paper has benefited from the advice and assistance of Wenhong Chen, Caroline Haythornthwaite, Philip Howard, Kristine Klement, Uzma Jalaluddin, Uyen Quach, Ann Sorenson, and Beverly Wellman. We especially acknowledge the help of Monica Prijatelj in preparing the tables and figures. Our compatriots at the University of Toronto's NetLab, Centre for Urban and Community Studies, Department of Sociology, Faculty of Information Studies, Knowledge Media Design Institute, and Bell University Laboratories have created stimulating milieus for thinking about the Internet in society. Research underlying this chapter has been supported by Communication and Information Technology Ontario, the IBM Institute of Knowledge Management, Mitel Networks, the National Geographic Society, and the Social Science and Humanities Research Council of Canada. This chapter is dedicated to S. Roxanne...
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".