Social Capital, Internet Use and Engagement
Bibliographic record
Abstract
This paper examines how Internet use relates to social capital and its outcomes- civic and political engagement. This topic has received much attention recently, but the concept of social capital needs clarification to enhance the quality of empirical examinations. I use Coleman’s (1988) original formulation of social capital, which has three components: obligations, expectations and trustworthiness of structures; information channels; and norms and effective sanctions. Subsequent iterations of Coleman’s definition, such as Putnam’s (1993, 2000) work, have ignored the information channel component of social capital. This component, I argue, is critical to defining social capital and to understanding how the Internet could affect social capital and its outcomes. The Internet is a important mechanism of information gathering and information flow. Thus, it can be critical to the functioning of social capital as an information channel. Using the Canadian General Social Survey- Cycle 14 (2000), I test a causal model that examines how Internet use relates to trust as well as civic and political engagement. Internet use, particularly informational uses of the Internet, helps to predict political engagement. I compare Internet users and non-users, as well as general Internet use and informational uses of the Internet to more fully illustrate the role of the Internet in social capital and its outcomes- civic and political engagement. When comparing the various models, I find that the direct impact of education on political engagement is greatly diminished by introducing informational uses of the Internet to the model predicting engagement. I argue that by enabling the information channel component of social capital, Internet use can reduce the effects of education, which can expand levels of civic and political engagement in the population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".