Remembrance Tourism: Maarjamäe Memorial Versus The Estonian Victims of Communism Memorial
Bibliographic record
Abstract
The people of the Republic of Estonia experienced severe oppression and terror during the latter half of the 20th century following their forced annexation into the Soviet Union. Additionally, the Soviet military can rightfully be credited with decisively driving Nazi Germany out of Estonia, during World War II. These related, but conflicting results, has resulted in two different memorials, and two radically different perspectives, located within 500 meters of each other, in the Estonian capital city of Tallinn. This research examines the impact of such confrontation in ideals and remembrance, through the promotion (or lack of), funding, and maintenance of history, through memorials in public space. This research addresses these questions through a comparison of two Memorials located within sight of each other, the Maarjamäe Memorial and the Estonian Victims of Communism Memorial, in Tallinn, Estonia. The comparison of the two Memorials highlights the challenges involved in the construct of remembrance, as well as the related construct of nostalgia, within markets such as Estonia that has two distinct ethnic groups, Estonian, and Russian, and how their respective views of the constructs shape the success or failure of such tourism attractions. The findings of this research will be of benefit to other regions with a similar past, when it comes to remembrance and reflection through tourism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".