MétaCan
Menu
Back to cohort
Record W2605740642 · doi:10.1080/19407963.2017.1316728

Reimaging a post-conflict country through events – lessons from Northern Ireland

2017· article· en· W2605740642 on OpenAlexaff
Adrian Devine, Karla Boluk, Frances Devine

Bibliographic record

VenueJournal of Policy Research in Tourism Leisure and Events · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGovernment (linguistics)Foreign policyPoliticsPolitical scienceDiplomacyPolitical economyDevelopment economicsEconomicsLaw

Abstract

fetched live from OpenAlex

War can have an instant and far-reaching negative effect on the image of a country. Gilboa [(2006). Public diplomacy: The missing component in Israeli’s foreign policy. Israel Affairs, 12(4), 715–747] discusses how a country involved in a prolonged violent conflict will acquire an undesirable, hard image that has to be softened. To try and achieve this, an increasing number of governments have, or are considering including events as part of their post-conflict recovery strategy. This paper focuses on Northern Ireland and how it has strategically used major events as a policy tool to help remove stereotypical images of its troubled past. The paper also highlights the risks of using events in a post-conflict environment. The authors recommend that if a government of a post-conflict country does decide to invest in events for reimaging purposes, it must do so with caution. Events and the media attention they attract help project the image of the place. However, if there are still political tension and entrenched problems within the country, then a negative image may be portrayed.

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 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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.155
GPT teacher head0.495
Teacher spread0.339 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Policy Research in Tourism Leisure and EventsSame topicSport and Mega-Event ImpactsFrench-language works237,207