Portfolio of Major Events in Auckland, Wellington and Dunedin
Bibliographic record
Abstract
The overall purpose of this chapter is to analyse the inter-relations between institutional arrangements, event policy frameworks and applied portfolio approaches. The chapter aims to explore the influence of the public sector institutional and policy environments on the realisation of portfolio approaches in three cities in New Zealand, Auckland, Wellington and Dunedin. The cities have a core national status (Ministry of Business Innovation and Employment, 2012) in terms of economic, political and socio-cultural share, and represent a variety of different contexts. Auckland is located in the North Island of New Zealand. It is the largest urban area in the country with a population of 1,415,500. It contains around 190 ethnic groups. Auckland is New Zealand’s principle business centre and accounts for 35.3% of New Zealand’s GDP as major national gateway for imports and exports (Statistics New Zealand, 2014). It is the most visited tourist destination in New Zealand, attracting around 70% of all visitors to the country (aucklandnz.com, n.d.). Auckland has been recognised in different international comparative studies such as Mercer Quality of Living Survey is 2015 and 2018, where it was ranked the third most liveable city in the world (Mercer, 2015, 2018).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".