Bibliographic record
Abstract
Lacrosse was a popular Native American sport in the Pre-Columbian era in North America before European settlement (Eitzen & Sage, 2009). This sport has a rich tradition as the Canadian Parliament recognized it as Canada’s “national summer sport” (Vennum, 1994). In America, lacrosse has been a widely popular sport in much of the New England region for decades. More recently, the sport has gained popularity in other areas of the country, like California and Colorado. This sport at the collegiate level also received national attention, since more games have been televised on the Entertainment and Sport Programming Network (ESPN) channels as part of National Collegiate Athletics Association (NCAA) Championship series. While the sport is continuing to grow outside of its traditional geographic area, it is still struggling to attract a more diversified group of athletes. Lacrosse, which has been around in some form for almost 300 years, is still very much dominated by white athletes. Embracing diversity has been a consistent educational philosophy and goal that is emphasized by the NCAA. Therefore, the lack of minority participation in lacrosse is an issue that is closely scrutinized by the NCAA’s Office of Diversity and Inclusion. According to the NCAA Student Athlete Race/Ethnicity Report (NCAA, 2010), white athletes made up 91.4 percent for male lacrosse players and 90.6 percent of female players. Black athletes, however, made up just 2.2 percent of male athletes and 2.4 percent of female participants at the Division-I level. Less than 10 percent of the student-athletes playing NCAA lacrosse were black. That statistic carries over to both men's and women's lacrosse in all three divisions (Ricardi, 2011). To help the readers understand the diversity issue concerning this particular sport, the authors will address three questions in this essay: (1) What are the challenges in trying to diversify the sport of lacrosse? (2) What progress has been made? And (3) what can be done to encourage more participation from minority athletes with different backgrounds, races, and ethnicities?
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".