Commemoration And Poppies: Cambridge, and Other American Battle Monuments Commission Cemeteries’ Mission in Anniversary Years
Bibliographic record
Abstract
"The English language does not have the words to describe something as powerful as this1" Around the beginning of August 2014, the grassy moat around the Tower of London became a field of red ceramic poppies to commemorate the centenary of World War I. As Remembrance Day (Veterans Day, in the United States) on November 11 approached, millions travelled from all over the London area, the country, and indeed the world, to view 'Blood Swept Lands and Seas of Red.' This display became so popular that the Mayor of London, as well as the leaders of the three major political parties and many average citizens, petitioned for the display to remain in place even after Remembrance Day. Meanwhile, across the Atlantic, in the nation where its native son John McCrea, the surgeon from Toronto who wrote the famous World War I poem, “In Flanders Fields the poppies blow,” immediately after the October 22, 2014 terrorist attack in Ottawa on Parliament, there was a push for the Royal Canadian Legion to start selling poppies earlier than normal for November 11 Remembrance Day (Yuen, 2014). While Remembrance Day is commemorated every year, 2014 took on heightened importance as the year was the 100th anniversary of the beginning of World War One. Clarke and Eastgate (2011) found in their research that the “sense of anniversary” (40) added “to the cultural value” of visits to commemorative sites. The recent list of these anniversaries and commemorations is lengthy. In June 2014, the 70th anniversary of D-Day was celebrated. More recently, related Victory in Europe and “VJ” days were commemorated. Eight hundred years ago, the foundation of English law, the Magana Carta was signed. In 2015, the Queen visited the Runnymede site and major exhibits were on display around Britain. In addition, Belgium saw huge numbers of tourists for the June 2015 reenactment and associated ceremonies on the 200th anniversary of the Battle of Waterloo. The Belgian city of Ieper (Ypres) sees an important contribution to its economy based on the daily “last post” ceremony at the Menin Gate which has seen even more attendees at the ceremony during the centenary. Visitors are often surprised at how many actually go to attend. Comments such as, “I was surprised by the number of people at the memorial. I arrived half an hour before the Last Call and there was a very large crowd already gathered at the memorial,” often appear on TripAdvisor in relationship to the Last Post ceremony.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".