Defining contribution: A scoping review
Bibliographic record
Abstract
INTRODUCTION: The term contribution is a key feature of approaches to positive youth development but has yet to be adequately defined. The purposes of this study were to identify properties that underlie contribution and to establish a theoretical definition of the construct. METHODS: A two-phase scoping review was conducted. The first phase involved the systematic search, selection, and analysis of studies from which six properties of contribution were identified and a preliminary theoretical definition was constructed. In the second phase expert judges rated the degree to that the proposed definition and the six properties of contribution fit with their conceptualization of the construct on a 6-point scale. RESULTS: Mean ratings for the definition and the six properties ranged from 5.05 to 5.70. Judges' ratings for the overall definition of contribution and the six properties were evaluated using Aiken's content validity coefficient (V). All V coefficients ranged in size from 0.81 to 0.94 and were statistically significant (p's < .01) indicating that the definition of contribution and the corresponding six properties fit well with experts' conceptualizations of the construct. CONCLUSION: This study provides a theoretical definition of contribution that will serve as a foundation upon which future research in the area of positive youth development can build.
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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 | 0.059 | 0.170 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.033 | 0.029 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".