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Record W4281616551 · doi:10.34190/ictr.15.1.374

Remembrance Tourism: Maarjamäe Memorial Versus The Estonian Victims of Communism Memorial

2022· article· en· W4281616551 on OpenAlexaff
Brent McKenzie

Bibliographic record

VenueInternational Conference on Tourism Research · 2022
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEstonianCommunismAnnexationTourismUkrainianConstruct (python library)Political scienceNazismThe RepublicOppressionHistoryEconomic historyGender studiesLawSociologyPolitics

Abstract

fetched live from OpenAlex

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.620
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0180.000

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 head0.188
GPT teacher head0.435
Teacher spread0.247 · 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.

Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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