Bibliographic record
Abstract
Using databases available on the Internet, the number of scientific papers on the subject of field hockey were examined. Basic procedures. As a result, 208 scientific studies covering the fields of biochemistry, physiology, sport injuries, psychology and tactics were found, which were published within the last 50 years (from 1960 to 2010). Given field hockey's success and recognition as an Olympic sport, the number of research articles focused on field hockey was significantly lower relative to the number of publications on other organized sports, such as soccer, basketball, or baseball. It was found that the highest number of publications (61.06 per cent) came from five English-speaking countries (UK, USA, Canada, Australia, and New Zealand), with the rest focusing on sport psychology, injuries, and biochemistry. What was discovered was that in comparison with other team sports, the vast majority of scientific studies used field hockey merely as a reference. The differing subject complexity of the research studies contained in the repositories greatly impedes an accurate comparison of results, particularly given that most of the studies focused only on a few chosen facets of the question matter and were, or were not replicated, mostly small sample studies.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".