Sport Organisations ‟ use of the Web 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 1 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 relationshipbuilding 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".