Does Purpose Grow Here? Exploring 4-H as a Context for Cultivating Youth Purpose
Bibliographic record
Abstract
Most youth development programs strive to promote thriving, but scientific inquiry into how they achieve this aim is rare and often complicated by nuanced program structures and delivery. Across two studies, we explored how one thriving indicator, having a sense of purpose in life, may be cultivated by a statewide 4-H program. In Study 1, an inductive text mining approach called latent Dirichlet allocation (LDA) was used to content analyze 63 4-H practitioners’ definitions of purpose and focus group conversations about how the program fosters this sense in youth. In Study 2, 113 4-H participants (aged 12–18 years, M age = 14.77; 66% female) reported their purpose exploration and commitment and the extent to which they have engaged with particular program experiences. The LDA suggested educators believe 4-H fosters purpose by offering diverse and transformative activities that equip youth with key resources. Youth reports largely corroborated these beliefs: Correlational analyses revealed youth who felt they acquired life skills in 4-H reported greater purpose commitment, whereas youth who felt they had access to older youth with long-term aspirations reported greater purpose exploration. Implications for how 4-H and other programs might scaffold activities to promote youth purpose are discussed.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".