Issues in the management of voluntary sport organizations and volunteers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Australia, Canada, parts of Europe, and the United Kingdom, the provision of sport has a long tradition of reliance on volunteers. Volunteers perform a variety of duties ranging from coaching, maintaining grounds, and providing transportation through to senior management and development roles such as chairpersons, club secretaries, and treasurers. Volunteers come from a variety of backgrounds: some are (ex-)players who wish to pass on the experiences that they received; some are parents supporting their children’s involvement; and others are individuals helping their local community (Cuskelly et al. 2006a). Voluntary sport organizations (VSOs) vary considerably in size and complexity. The largestarea of sports volunteering activity is within sports clubs run by their members, and the majority of these operate within a governing body structure. In England there are over 100,000 sports clubs run by volunteers, involving over eight million volunteers (Taylor et al. 2003). In Australia, over 1.7 million people volunteer in sport and over a third of these contribute 140 hours or more of their time each year (ABS 2009). Research in European countries suggests that between 2.6 per cent (France) and 6 per cent of the population regularly volunteer in sport (see Coalter 2007). Volunteers are important in all facets of the sports governing body structure which may have local, regional, and national levels; even at national governing body level, volunteers play critical roles as administrators and policy makers. The relative importance of paid staff varies considerably between national governing bodies (NGBs): the few wealthy ones, such as the Rugby Football Union in England, employ considerable numbers of paid staff, both centrally and across the country, but small NGBs rely almost entirely on volunteers. In relation to this chapter, the most important feature of volunteering within sports clubs is that the clubs are relatively small and are run by volunteers themselves. Paid staff are most likely at the NGB level and, while their influence over clubs is restricted by the considerable autonomy of the clubs, they have indirect ‘control’ by directing and implementing policy. Another important area of sports volunteering is events. These vary far more in size thando sports clubs. The 2012 London Olympics will require 70,000 volunteers; the nearest comparable event in the UK, the 2002 Commonwealth Games in Manchester, required 10,500 volunteers (Ralston et al. 2004). However, there are innumerable small local events, run by clubs, local government, or a wide variety of other organizations. Unlike sports clubs, events are more likely to be managed by paid staff. Secondly, they are more likely to involve volunteerswhose commitment is restricted by time and event; what has been termed ‘episodic volunteers’ (Auld 2004). From an academic perspective, the struggle to manage volunteers and VSOs appears to stemlargely from incomplete understandings of what it means to volunteer and the process of managing sports organizations. This chapter explores three ‘management’ issues currently facing sport: first, the differences between managing VSOs and other types of organizations; second, the differences between managing volunteers and employees; and, finally, the differences in managing episodic volunteers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it