Bibliographic record
Abstract
Purpose The purpose of this paper is to review the study of social capital focused on the level at which it is embodied, cross-comparing two prominent camps that have emerged in the social capital literature: a communal level and an individual level. Design/methodology/approach This paper reviews the intersections and departures between communal level and individual level conceptualizations of social capital according to the social dynamics of action within social exchanges that they stimulate, the processes by which social capital is activated/mobilized and the rewards they yield, and their linkages to inequality through network diversity. Findings This paper articulates new directions for future research in social capital: more analytical precision for studying returns to social capital; more efforts to transcend the individual-communal divide; the depreciation of social capital or tie decay; and recognizing the importance of ties whose value does not come from the ability to provide instrumental gain, but just from their very existence. Originality/value Social capital has informed many influential agendas in the social sciences, but the sheer volume of which has largely gone unscoped. This paper reviews this literature to provide an accessible introduction to social capital, organized by social processes foundational to sociology and a novel contribution to the literature by articulating new directions for future research in the area.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".