Research and publication trends: Sports branding on the movie
Bibliographic record
Abstract
Many films have raised the story of sports as the major story or as the background of a film. However, so far, no research has been obtained that analyzes the mapping of research results related to film and sports. Therefore, this study intends to examine various studies related to film and sports that global researchers have produced. This research uses the bibliometric method. The data source used is the Web of Science, while the tools used to process and display the data are ScientoPy and VosViewer. The results showed that the development of scientific publications starts 2000 to 2021 related to films and sports experienced fluctuating developments. Authors from America and England occupy the top positions for the number of publications. However, in the period 2020 and 2021, researchers from France and Canada are researchers who are more productive in publishing their scientific works. They included most published scientific works related to films and sports in the WoS version of the Social science category. However, Between 2020-2022, more are categorized into the subject of Communication and Film, Radio, & Television. Reference sources widely cited in scientific publications related to film and sports are books by Beeton with the title film-induced tourism (2005) and Crosson's work with the title sport and film (2013). The results of the keyword mapping show that six clusters represent some keywords used by the author in scientific works related to films and sports. In conclusion, sports and cinema research are fully developed in this research.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.024 | 0.052 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".