Bibliographic record
Abstract
Knowledge is socially constructed, and one way that researchers convey knowledge is through citation practices within research texts to illustrate the foundation upon which current research is designed and results interpreted. Citation network analysis (CNA) is a review method that seeks to map the scientific structure of a field of research as a function of citation practices. Generally speaking, research texts that receive more citations from others symbolizes a degree of prominence to a field of study; however, the more common approaches to synthesizing research in the form of a review (e.g. meta-analyses, systematic reviews) are not able to capture these underlying metrics. Given that CNA is relatively new to the field of sport and exercise psychology, we first provide an overview of the method, including a brief review of network theory, existing research in the field of sport and exercise psychology, and some of the important limitations to consider. Then, we offer a series of guidelines to direct CNA reviews from the conception of a research question to the visualization of a citation network. Finally, we conclude the review with an overview of recent methodological advancements with potential to expand research questions and benefit future citation network research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.050 | 0.057 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.011 |
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".