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Record W2301171167

A Study of Battlefield Tourists in Leper, Belgium

2010· article· en· W2301171167 on OpenAlexaboutno aff
Caroline Winter

Bibliographic record

VenueCAUTHE 2010: Tourism and Hospitality: Challenge the Limits · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Tourism and Spaces
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)BattlefieldHonourTourismGratitudeHistoryMedia studiesSample (material)GeographyAdvertisingVisual artsEthnologySociologyArchaeologyAncient historyArtPsychologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

In the Great War of 1914-18 the town of Ieper (Ypres) was the centre of the horrific battles of Passchendaele, and today, it is a large centre for battlefield tourism. This study is part of a broader project to analyse some of the motivations and experiences of people who visit the battlefields and their various memorials and museums. The sample was selected from visitors attending the Information Centre located in the Cloth Hall in the town of Ieper. Most respondents had relatives involved in war, but only about one quarter were intending to visit a cemetery or wall of names to trace a soldier. Very few people classified themselves as pilgrims, and nearly half preferred to describe themselves as a tourist. The responses to qualitative and quantitative questions revealed that most visitors were motivated to visit the town by opportunities for education and historical interest about the war, and to show gratitude and honour those who had fought. Approximately one quarter of the sample were not touring the battlefields, reflecting the other attractions that the area holds.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0070.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.306
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2010
Admission routes1
Has abstractyes

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