Bibliographic record
Abstract
The increasing use of planned events by cities, regions and countries worldwide to achieve their policy goals and obtain economic, tourism, place-marketing, or broader community benefits has led to the creation of city-wide programmes staging a series of recurring events all year round. The strategic intent of host communities and destinations to manage a calendar of events engenders the development of event portfolios. For example, the cities of Edinburgh (City of Edinburgh Council, 2007), Gold Coast (City of Cold Coast, 2011) and Auckland (ATEED, 2018) have developed, their own strategic portfolios by assembling and coordinating a balanced number of periodic events of different type and scale. Portfolio strategies have also been employed on national level, for example, in Wales (Welsh Government, 2010), Scotland (Visit Scotland, 2015) and New Zealand (Cabinet Office Wellington, 2004). The endeavour of places to develop event portfolios lies upon the alignment of their event strategies with their policy agendas. In so doing, the underlying rationale is to create a diversified portfolio of events that take place at different times of the year and that appeal to audiences across the span of consumer profiles which a host destination seeks to target (Chalip, 2004; Getz, 2013; Ziakas, 2014). From this standpoint, multiple purposes can be achieved by leveraging the event portfolio and fostering synergies among different events and their stakeholders in order to optimise the overall portfolio benefits and value.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.471 | 0.281 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".