Leisure Cycling Entrepreneurialism in Johannesburg, South Africa
Bibliographic record
Abstract
It is estimated that by 2050 as many as five billion bicycles could be in use globally. Reasons for this growth vary, with utility cycling strong in Europe and Asia; while in the United States of America, Canada, the United Kingdom, Australia and New Zealand cycling is a sport and leisure activity, with cycling deemed the ‘new golf’. Within this context, there is a rise in community or local bike shops (or LBS) which sell bicycles, related equipment and services. This qualitative study explored LBSs in greater Johannesburg, focusing on who the entrepreneurs are, how they service their clients, and what links the sector has to serious leisure. It was found that the owners demonstrated high levels of serious leisure cycling engagement and passion for the sport. A sense of being part of a bigger cycling community strongly influenced their entrepreneurial practices. This included how they ran their businesses, the employees they hired, as well as how they viewed cycling in general. The research also yielded insights into operational and sectoral realities, trends and challenges. Generally, it was found that the local bike shops in greater Johannesburg are key players in the supply, growth and development of sport and leisure cycling, thereby making a positive contribution to the cycling community. This is important in the light of the COVID-19 challenges experienced by the sport and leisure sector, as a loss of these shops will likely have a negative impact on cycling in Johannesburg.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".