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Record W77919102 · doi:10.4324/9780203885581-43

Issues in the management of voluntary sport organizations and volunteers

2010· book-chapter· en· W77919102 on OpenAlexaboutno aff
John Schulz, Geoff Nichols, Christopher John Auld

Bibliographic record

VenueRoutledge eBooks · 2010
Typebook-chapter
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
Fundersnot available
KeywordsClubCoachingVoluntary associationVariety (cybernetics)PopulationPolitical scienceTurnoverPublic relationsSport managementPsychologyManagementSociologyMedicineDemographyLaw

Abstract

fetched live from OpenAlex

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.076
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.076
Threshold uncertainty score0.401

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0760.111
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0240.028
Scholarly communication0.0300.019
Open science0.0080.018
Research integrity0.0140.010
Insufficient payload (model declined to judge)0.0130.002

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.

Opus teacher head0.014
GPT teacher head0.274
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations4
Published2010
Admission routes1
Has abstractyes

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