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Record W2085473157 · doi:10.1017/s0032247412000691

Ittoqqortoormiit and the National Park of Greenland: a community's option for tourism development

2013· article· en· W2085473157 on OpenAlexaboutno aff
Daniéla Tommasini

Bibliographic record

VenuePolar Record · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTourismSubsistence agricultureNational parkCommunity developmentUnemploymentGeographyBusinessEcotourismTourism geographyEnvironmental planningEnvironmental resource managementEconomic growthAgricultureEconomicsArchaeology

Abstract

fetched live from OpenAlex

ABSTRACT Landscape and culture are among the topics that make a place attractive for tourists. Protected areas, used also for leisure purposes, represent good development opportunities for the neighbouring communities. The National Park of Greenland has adopted new regulations and leisure activities will be allowed in its area. This will represent an opportunity of increasing the tourism business in Ittoqqortoormiit, the adjacent community to the park, which is suffering from a difficult economic situation. This new use of the resources in the protected area may well serve as engine for growth and revitalization of the local economy that has a chronic lack of jobs and an important outmigration. In this article are presented some of the results of interviews done in 2009 with the Inuit of Ittoqqortoormiit regarding tourism. The goal of the project ‘Community-based tourism as an option for concrete, viable development in peripheral, remote places’ was to investigate how a small Inuit community, peripheral and remote, which has traditional subsistence activities, but low incomes and high unemployment rate, could seek economic alternatives in 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 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)0.000
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 score0.104
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.049
GPT teacher head0.320
Teacher spread0.271 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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