Birdie, Par or Bogey? The State of Golf in North America
Bibliographic record
Abstract
UWill Discover 2016 Abstract Submission Emily Stadder & Dr. Jess C. Dixon Birdie, Par or Bogey? The State of Golf in North America Currently, there is a large amount of research on golf throughout North America, however it is fragmented. This poster will be useful in forming a better picture of the true state of golf in North America. The methodology for these findings was an extensive literature review looking at reports, articles, trade journals, and previous studies. Both qualitative descriptions and quantitative measures were used to assess the state of the industry. Within this study, key areas examined pertaining to golf in North America were: popularity, socioeconomic impact, facilities, consumer behaviour, and future directions. Currently, in popular media the golf industry is often depicted as suffering and on the verge of extinction. However, this may not be the case. Like most industries it was hit hard with the 2008-2009 economic collapse, but has since seen improvement. At this time golf in North America is at a cross-roads. It will suffer if it is not open to change, but the industry also has potential to grow, if it is able to interest younger generations and become more inclusive. Key Words: Golf, North America, Canada, United States of America, golf industry, future directions
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".