Canadian national sport organisations’ use of the web for relationship marketing in promoting sport participation
Bibliographic record
Abstract
Sport participation development requires a systematic process which involves knowledge creation, dissemination and interactions between National Sport Organisations, participants, clubs and associations as well as other agencies. Using a relationship marketing approach (Grönroos, 1997, Gummesson, 2002, Olkkonen, 1999), this paper addressed the question ‘How do Canadian NSOs use the Web, in terms of functionality and services offered, to create and maintain relationships with sport participants and their sport delivery partners?’ Ten Canadian NSOs’ websites were examined: functionality was analysed using Burgess and Cooper’s (2000) eMICA model, while NSOs’ utilisation of the Internet to establish and maintain relationships with sport participants was analysed using Wang, Head and Archer’s (2000) relationship-building process model for the Web. It was found that Canadian NSOs were receptive to the use of the Web, but their information-gathering and dissemination activities, which make-up the relationship-building process, appear sparse, and in some cases are lagging behind the voluntary sector in the country.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".