Bibliographic record
Abstract
This paper, from the Dr. Earle F. Zeigler Award Lecture presented at the NASSM 2012 Conference in Seattle, outlines the merits and challenges of interdisciplinary research for the field of sport management. This alternative approach involves relating, integrating, and relocating disciplinary thinking to arrive at a mutually-determined research problem that represents new ways of conceptualizing phenomena. It enables moving away from the monodisciplinary research that characterizes much of our field to examine phenomena from different angles, and perhaps more effectively close the research-practice gap with knowledge derived from multiple perspectives. The author argues that it is time to engage in interdisciplinary research in sport management as no one discipline has all the answers; rather, “it takes a village” to solve the complex problems in our world.
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 | 0.037 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.062 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".