The Role of Social Capital Theory in the Development of Effective Religious Volunteerism
Bibliographic record
Abstract
This study considers the impact of church involvement on social capital (SC) levels, and the impact of these levels on church volunteerism. SC theory suggests that relationships are established with a purpose in mind and that the aim of the individual is to use these relationships in a beneficial way. This theory drives the research hypotheses and findings of the study. It is hypothesized that church involvement has a positive correlation with higher levels of SC, and that SC has a positive correlation with volunteerism. SC, therefore, is seen as a mediating variable linking increased church involvement with increased volunteerism.Cette étude prend en considération l’impact de l’implication religieuse sur les niveaux de capital social et l’impact de ces niveaux sur le bénévolat religieux. La théorie du capital social suggère que les relations s’établissent avec un but en tête et que l’objectif des individus est d’utiliser ces relations de manière bénéfique. Cette théorie est le moteur des hypothèses de recherche et des résultats de l’étude. L’hypothèse de l’étude est que l’implication dans les institutions religieuses a une corrélation positive sur un niveau plus élevé de capital social et que le capital social a une corrélation positive sur le bénévolat. Le capital social est donc vu comme une variable médiatrice entre un accroissement de l’implication religieuse et l’augmentation du bénévolat.
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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 | 0.010 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".