“What Have I Learned … ” and How Did I Get There? Reflection on a Research Journey
Bibliographic record
Abstract
Receiving a lifetime award allows one to pause and reflect on one’s research journey. In the spirit of Earle Zeigler himself, I reflect on: “What I have learned … ” on my research journey, and more specifically on how I got there. My research has always focused on the interaction between sport, economics, and society and evolved: “From socio-economic impacts on sport participation to socio-economic outcomes of sport events.” To cover 40 years of research, I am highlighting how: (a) “triggers,” (b) “influencers,” and (c) “lessons learned” intermingled to push my research agenda forward. This reflection proved to be a very gratifying exercise. I can highly recommend it to all researchers. Perhaps, this can become a stepping stone to be promoted to the rank of Prof. Emeritus or Emerita. Either way, sharing our experiences may trigger, inspire, and advance the learning of future generations of sport management scholars.
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 | 0.041 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.030 |
| Scholarly communication | 0.038 | 0.043 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.012 | 0.039 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".