“I am not too old to play” – The Past, Present and Future of 50 and Over Organized Sport Leagues
Bibliographic record
Abstract
Abstract The ageing population in Canada is dramatically increasing. According to recent demographic projections, roughly 20 percent of Canada’s population will consist of people over the age 65 by 2024. Indeed, the senior population is expected to surpass that of children under the age of 14 by 2017. This growth of the senior cohort signals opportunities for individuals over the age of 50 to challenge stereotypes and embrace active living. Organized sport leagues are a means for seniors to not only embrace active living, but to also re-live and continue living the competitive sports that they played earlier in life. The increasing number of organized sport leagues for this cohort, including the active living philosophy embraced by baby boomers, will probably lead to an increased demand for more organized sport opportunities for this population group. The purpose of this paper is to provide a current state of condition of organized sport leagues for those 50 years of age and over. Specifically, the objective of this paper is to present the evolution of organized sport leagues for those 50 and over while also making suggestions for the future provision of such services. It is concluded that: a) more research is needed to better understand the trend of 50 and over sport leagues, b) municipal sport and recreation administrators should consider establishing more 50 and over sport leagues in their recreation program delivery systems, c) 50 and over sport leagues should better address the needs of specific population groups (e.g., women and ethnic groups), and d) awareness should be enhanced for potential entrepreneurial opportunities for the establishment of 50 and over sport leagues.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".