Network Diversity and Political Participation: A Complication or an Asset?
Bibliographic record
Abstract
Social capital researchers have suggested that bridging ties, i.e. networks of people with different opinions and backgrounds, are important for democratic politics. Yet, while some studies show a positive effect of network diversity on political mobilization, others tend to suggest a dampening, rather than a mobilizing effect. Exactly which type of network diversity matters and how diversity exerts its effects is often under-researched, especially in a comparative setting. This article examines how different types of network diversity in strong and weak ties influence political participation among young people in Canada and Belgium. The study draws on the Comparative Youth Survey (CYS) which includes almost 10,000 young people between the ages of 15 and 17 in these countries. The results reveal that particularly political and socio-economic network diversity in both strong and weak ties mobilize political participation among youth. Political knowledge, interest and discussion are identified as mediating these network effects.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".