Reimaging a post-conflict country through events – lessons from Northern Ireland
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".