Taking stock of positive youth development research in sport: A citation network analysis
Bibliographic record
Abstract
Although sport has been established as a vehicle for positive youth development (PYD), an overview of this broad literature base reveals a range of conceptual and theoretical orientations guiding the research. In order to synthesize the sport-based PYD literature, a citation network analysis was conducted to identify the most prominent publications in the area and to evaluate the interconnectedness of the literature based on citations. Peer-reviewed quantitative and qualitative journal articles (N = 197; search finalized June 2018) were included if they (a) used PYD research to establish the conceptual context for the study, (b) included organized and adult-supervised competitive sport, recreational sport, or other settings that included sport activities (e.g., summer camps, school/after-school programs), and (c) reported at least one outcome measure relevant to PYD. Articles within this list then were coded based on whether they cited (1) or did not cite (0) one another, and network metrics were summarized using UCINET software. The network structure was visualized and a modularity algorithm detected four research communities within the PYD literature. These communities, including their total citation network coverage and most cited article were: life skill development (31.9%, 324 total citations; Holt et al., 2008, 29 citations), developmental/sport experiences (27.6%, 246 total citations; Larson et al., 2006; 44 citations), PYD programming (25.5%, 152 total citations; Brunelle et al., 2007; 27 citations), and social environment (15.0%, 110 total citations; Fraser-Thomas & CA´tA©, 2009; 19 citations). Implications of these findings are addressed including the need for common conceptual ground in PYD research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".