Access to Expertise as a Form of Social Capital: An Examination of Race- and Class-Based Disparities in Network Ties to Experts
Bibliographic record
Abstract
Social capital theory suggests that individuals can access resources through their relationships with others. While research in this area typically focuses on the potential benefits of having high-status network alters, the authors emphasize that relationships with experts, in particular, provide access to specialized knowledge. Expertise may be accessed through formal, contractual means. But individuals who have an expert within their network of close family and friends may benefit from more convenient and lower cost expertise. The authors explore the prevalence and nature of expert contacts within individuals' social networks using data from the 1985 and 2004 General Social Surveys. About a quarter of Americans identify an expert among their network contacts. Racial minorities and members of the lower- and working-classes have less access to experts within their personal networks, however, and minorities have become particularly disadvantaged over the past two decades in terms of both overall and informal access to expertise. The authors urge further research to examine the causes of disparities in social network ties to experts and their implications for processes of social stratification.
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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 | 0.005 |
| 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 | 0.001 |
| 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".