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Introduction

2019· book-chapter· en· W4238730359 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGoodfellow Publishers eBooks · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPortfolioDestinationsWelshTourismCabinet (room)AppealGovernment (linguistics)Event (particle physics)GeographyBusinessMarketingPolitical scienceFinance

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.389
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.025
GPT teacher head0.255
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it