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Record W2136318653 · doi:10.5539/ep.v2n2p1

Local Attitudes on Protected Areas: Evidence from Sumava National Park and Sumava Protected Landscape Area

2013· article· en· W2136318653 on OpenAlexvenueno aff
Tomas Gorner, Martin Čihař

Bibliographic record

VenueEnvironment and Pollution · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismNational parkIndigenousGeographySocioeconomicsResidenceProtected areaEnvironmental planningEnvironmental protectionEnvironmental resource managementEcologySociologyDemography

Abstract

fetched live from OpenAlex

The present article aims to describe perceptions and awareness of local residents in two categories of the Sumava region protected areas–National Park (NP) and Protected Landscape Area (PLA). The survey explores perceptions of individuals on nature protection, protected area management, tourism and related issues. Differences between these two research areas are also explored. Standardised personal interviews were conducted during the summer season of 2008. The study took place in six municipalities in NP (Borova Lada, Srni, Prasily, Kvilda, Horska Kvilda and Modrava; 183 questionnaires in total) and in three municipalities in PLA (Kasperske Hory, Hojsova Straz and Cachrov; 138 questionnaires in total). According to the results of the study, there were more natives and indigenous residents in NP than PLA. Similarly, local people in NP were working more often in the public sector and less in the private sector and they had more benefits from tourism. Residents in PLA were less informed about Administration activities, more satisfied with topical nature conservation level and against expansion of NP to their place of residence. Also, a significant finding of the study is that residents (especially in PLA) were supportive of some forms of participatory management. They are interested in the advancement of the area, mostly in the form of improvement of tourism-related facilities. They love this region and also call for better communication with NP/PLA Administration.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.278
Teacher spread0.243 · 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 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

Citations9
Published2013
Admission routes1
Has abstractyes

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