Enabling Event Volunteer Legacies: A Knowledge Management Perspective
Bibliographic record
Abstract
Human capital development delivered through the volunteers is espoused as one legacy outcome of hosting mega-sporting events such as the Olympic and Paralympic Games. However, to date the reality of such a legacy remains largely undemonstrated. In this article, Nonaka and Tacheuchi's SECI model and Lee and Yang's knowledge value chain (KVC) are integrated to identify insights to support the development of a potential human capital legacy from volunteers in future mega-sport events through focusing on knowledge management. A case study of the Vancouver 2010 Olympic and Paralympic Winter Games demonstrates gaps in the knowledge management systems in place, both in terms of the identification of knowledge and the processes for capture and reuse. It is argued that, unless those involved in hosting the events reconsider their approach to human capital legacy development, using the creation and management of knowledge as a core element, it is unlikely that long-term human capital legacy outcomes will be achieved for host communities.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".