Predictors of Early Community Involvement: Advancing the Self and Caring for Others
Bibliographic record
Abstract
Abstract Recent research on community involvement points to the importance of both agentic (advancing the self) and communal motives (serving others) as key predictors, though few studies have examined both simultaneously. At the same time, research has identified generativity, defined as concern for future generations as a legacy of the self, as particularly relevant for community involvement. Moreover, generativity involves both agentic and communal motives, meaning that advancing personal goals and caring for others are integrated in this construct. Therefore, the purpose of this study was to examine how individual differences in attributes pertaining to self and to others—specifically, self‐esteem, initiative, and empathy—related to both generativity and community involvement. A sample of adolescents (N = 160; 64% female, Mage = 17) and a sample of young adults (N = 237; 84% female, Mage = 20) completed a survey including measures of community involvement and generativity. Generative concern fully mediated the associations between individual differences (self‐esteem, initiative, and empathy) and community involvement, suggesting that the early generativity has a role in fostering capacities and contribution in youth. These developmental indicators pertaining to self and others link to actions that benefit the community through a desire to benefit future generations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".