Remembering World War I: Memory Influences and Impact on Intentions to Visit War Heritage Sites
Bibliographic record
Abstract
This paper presents partial results of a large-scale multi-lingual (English, French, German, and Dutch) international study conducted in 2012, resulting in responses from over 60 countries (n=2490). This paper provides analyses of data obtained from respondents in nine countries (Australia, Belgium, Canada, France, Germany, the Netherlands, Switzerland, United Kingdom, and the United States), which were involved in and impacted by the First World War (WWI). Eight factors influencing respondents’ memories of WWI (school lessons, TV news and documentaries, Internet, literature/arts, visits to WWI sites, story-telling, inheritance of memorabilia, and WWI movies), and impact of five demographic variables (country-of-origin, age, gender, education, and emotional proximity to WWI) on these factors are analyzed. Also examined is if how the way memories of WWI are formed relates to one’s intentions to visit a WWI heritage site in the near future and how it impacts support for granting UNESCO’s World Heritage Site (WHS) status to WWI battle fields. Results indicate that the way memories of WWI are formed vary by all five demographic factors and indicate that both intentions to visit a WWI heritage site in the near future and support for granting WHS status to WWI battlefields are related to how memories of WWI are formed. Implications for development and marketing of WWI heritage sites as tourism attractions are also discussed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".