Governmental Approach to Major Events in New Zealand
Bibliographic record
Abstract
For the past 20 years, New Zealand, a country relatively remote in geographi- cal terms, has been actively communicating with the international visitor market in order to construct a global brand for the country. As a tourist destination, New Zealand offers an impressive range of natural and cultural attractions, out- door activities, urban tourism and a diverse event calendar. In 2017, the country welcomed 3.7m visitors, with the market forecast to grow by 7.5% in 2018. The active role of the Government in the visitor economy makes New Zealand an attractive investment destination. Extensive marketing campaigns, significant expansion of transport connections, private investment in infrastructure and the hotel sector indicate that New Zealand will continue its sustainable tourism growth over the coming years. Major events have been recognised as a powerful and successful instrument that can brand the country directly to the target audience. The ever-increasing numbers of international event visitors to New Zealand, as well as recent success in securing bids for such large-scale international events as 2011 Rugby World Cup, 2015 ICC Cricket World Cup, 2015 FIFA U-20 World cup and 2017 World Master Games, demonstrate the relevance of the employed strategy. This chapter reviews a national event portfolio approach in New Zealand. The approach is characterised by a strong top-down orientation, where the Govern- ment plays the leading role in determining current economic and socio-cultural objectives for the major event industry, implementation of the national event strategy and evaluation of the investment in major events. The data for this chap- ter have been collected by document selection and analysis and by interviewing several industry experts.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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.
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".