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Record W4415586644 · doi:10.21083/crrf.v36i1.8090

Past and Present Informing the Future: A Case Study of Cultural Heritage Tourism in Louisbourg, NS

2025· article· W4415586644 on OpenAlexaffabout
Nicole Breedon

Bibliographic record

VenueProceedings of the Canadian Rural Revitalization Foundation · 2025
Typearticle
Language
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsBrandon University
Fundersnot available
KeywordsFortress (chess)Cultural heritageTourismHeritage tourismCultural heritage managementGeneral partnershipCultural landscape

Abstract

fetched live from OpenAlex

The importance of leveraging Cultural Heritage for the sake of knowledge preservation and exploration into the tourism sector cannot be understated. The Fortress of Louisbourg is an excellent example of rural tourism, as it continues to attract tourists from across Canada and beyond despite its remote location on Cape Breton Island. The Fortress was first declared a National Historic Site in 1928, but the partial reconstruction of this French Fortress is what attracted tourists to this municipality in the 1960s. In 2017, Dr. Amy Scott from the University of New Brunswick, in partnership with Parks Canada, established a bioarchaeological field school at Louisbourg to address ongoing coastal erosion, allowing students to build their bioarchaeological skills. Secondarily, ongoing bioarchaeological excavations have been supported and highlighted by Parks Canada to enhance the overall tourist experience. Throughout the 2023 bioarchaeological field season, surveys were administered to 30 tourists inquiring about their Fortress experience, and interests and values as tourists in Canada. Of these 30 guests, 96.7% indicated it was important for people traveling in Canada to visit Cultural Heritage sites, like the Fortress. These research findings exemplify how investments into local Cultural Heritage may provide communities opportunities to enhance economic development 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.251
Teacher spread0.221 · 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.

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
Published2025
Admission routes2
Has abstractyes

Explore more

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