A Content Analysis of the Journal of Legal Aspects of Sport: 1992-2016
Bibliographic record
Abstract
As the flagship journal of the Sport and Recreation Law Association, the Journal of Legal Aspects of Sport (JLAS) serves “…as an interdisciplinary outlet for legal issues in the sport, recreation, and related fields to meet the needs of researchers, academicians, practitioners, and policymakers” (About JLAS, para. 2). A study by Batista and Pittman (2006) identified JLAS as the most highly ranked sport law journal among journals focusing on sport management. Articles appearing in the Journal of Legal Aspects of Sport have informed court decisions and case outcomes, policy decisions and debate on limited liability legislation, health and safety issues, and universal access to sport opportunities (Spengler & Miller, 2014). Additionally, JLAS articles have been cited in journals published in a variety of countries including India, China, Australia, France, Canada, the United Kingdom, and Spain (Spengler & Miller, 2014). These inclusions indicate that the Journal of Legal Aspects of Sport has had some degree of success in attaining international recognition and appeal. Although JLAS has been published since 1992, a complete formal analysis of the content has never been conducted. A content analysis of JLAS may provide critical information regarding the diversity of topics covered, the specific research types utilized, demographic information regarding the authors published, and perhaps identify any gaps that may exist in the current literature base. Therefore, the purpose of this study was to conduct a content analysis of articles published in the Journal of Legal Aspects of Sport (JLAS) from 1992-2016.
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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.044 | 0.045 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".