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Record W2791532714 · doi:10.1111/cag.12439

Visual convincing of intangible cultural relationships using maps: A case study of the Tongariro National Park World Heritage nomination dossier

2018· article· en· W2791532714 on OpenAlexvenueno aff
Mark H. Palmer, Anna Feyerherm

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsNominationNational parkWorld heritageCultural heritageCultural propertyIntangible cultural heritageGeographyGovernment (linguistics)Industrial heritagePolitical scienceCultural heritage managementArchaeologyLawTourism

Abstract

fetched live from OpenAlex

UNESCO World Heritage site nominations require the use and presence of maps and GIS to demarcate potential heritage property boundaries. UNESCO and the World Heritage Committee provided specific cartographic guidelines and standards for the inclusion of maps within the nominations. The New Zealand government used maps and GIS to visually convince UNESCO, the World Heritage Committee, the International Union on the Conservation of Nature, and the International Council on Monuments and Sites of intangible cultural relationships at Tongariro National Park. More specifically, New Zealand combined scientific maps, Māori language narratives, and symbols to make the intangible tangible and geographically visible. Maps and GIS images that accompanied World Heritage nomination dossiers were housed at the UNESCO World Heritage Centre and the International Council on Monuments and Sites in Paris, France. The first section of the paper introduces the data sources and methods used in our archival research. Next, we provide a brief description of the Tongariro National Park nomination and present a case study on, and interpretation of, the maps and GIS contained within the Tongariro National Park World Heritage nomination dossier. Finally, we will offer some conclusions and directions for future research.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.011
Science and technology studies0.0040.004
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.288
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations4
Published2018
Admission routes1
Has abstractyes

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