Visual analysis of foreign ice hockey research based on knowledge graphs
Bibliographic record
Abstract
This paper takes the ice hockey literature included in the Web of science database from 1980-2022 as the data source, and uses Citespace5.8R3 visual software analysis tool, using literature, knowledge mapping, comparative analysis and other methods, to analyse the research on the development of ice hockey in foreign countries, aiming at presenting the status quo, hotspots and fronts of the research of ice hockey in foreign countries, and so on, and to provide for the development of ice hockey projects, research selection to provide reference. Findings: (1) The number of foreign ice hockey research publications is generally rising in waves, which can be divided into two phases: the slow growth phase (1983-2022) and the sharp growth phase (2013-2021) (2) The research is more in the countries with high competitive level of ice hockey programme, such as Canada, the United States, Sweden, etc. (3) The research reflects the cross integration of multiple disciplines and is distributed in the fields of engineering, sports science, neurology, psychology, sociology, social sciences, psychology, and so on. (4) The research hotspots are diffused from the initial sports injury and protection; athlete's age to the sports performance and hockey physical fitness.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.063 | 0.049 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".