Bibliographic record
Abstract
Results of the present survey ( n = 888) suggest that having strong social ties (or bonding social capital) fosters generalized trust, in support of conflict theory. There was no link between bridging social capital, or one’s more diverse ties, and trust. Facebook use was found to have an indirect but positive influence on trust through levels of bonding social capital. Civic engagement was also positively related to trust through the same measure of bonding social capital, allowing like-minded members of civic groups to connect, which spilled over to trust. Neither Facebook use nor civic engagement directly influenced generalized trust. This study suggests the viability of both physical (civic) and digital (Facebook) modes of reengaging trust in an ever-diversifying society. The “virtuous circle,” in which social trust and civic engagement prop one another up in a reciprocal fashion, instead looks like a figure eight here. Each measure indirectly boosted the other by first growing bonding social capital. When considered alongside divergent findings from Canada, this appears to be an American response to the increasing size of racial minority groups. Hispanic citizens make up the same portion of the American population as do all minority groups in Canada combined. These findings then represent a White reaction to an increasing Hispanic presence in America. Bounded solidarity in the form of strong, homogenous ties is shown as the path to trust in this setting.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".