Sport Management Research Productivity and Impact for Ranking Considerations
Bibliographic record
Abstract
The present essay aims to promote further dialogue within the sport management community about research productivity and impact by outlining various considerations that should take place within any potential ranking attempt. Some may question why examining research production and impact matters to sport management education, but the mission of many institutions of higher education is not exclusively centered on teaching and training the next generation of leaders. In many instances, sport management programs and faculty are collectively compelled by their host institution to develop theory and search for answers to important questions that can shape future sport management practices, including classroom activities and materials. In the present essay, a rationale is provided for why sport management programs and individual faculty should be interested in developing their own tailored research output and impact rankings. Next, a list of research product variables is offered for consideration, and a conversation is provided about their need and impact with respect to the uniqueness of sport management—a multi-interdisciplinary field. Finally, recommendations for the weighing of such variables to tailor an approach best suited to programs based on college or department home, faculty appointment/workload, and faculty-to-student ratio are submitted.
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 | 0.142 | 0.397 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.025 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.038 | 0.022 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".