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Record W2153370146 · doi:10.5539/ass.v6n9p64

Investigating the Perceptions of Residents in Golestan National Park, Iran

2010· article· en· W2153370146 on OpenAlexvenueno aff
Aryan Amirkhani, Bemanian Mohammad Reza

Bibliographic record

VenueAsian Social Science · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsNatural (archaeology)National parkEnvironmental resource managementGeographyEnvironmental planningNatural parkPerceptionVariety (cybernetics)BusinessEnvironmental protectionSocioeconomicsSociologyPsychologyEcologyEnvironmental science

Abstract

fetched live from OpenAlex

The natural characteristics of protected areas have changed for a variety of reasons through time. Changes in protected area landscapes can occur because of natural and/or cultural processes. Natural processes such as geomorphologic disturbance and climatic condition can permanently and/or temporarily change the characteristics of the environment. In addition, changes in human needs, knowledge and activities are the cultural driving forces behind changing characteristics of landscape through time. Herein, the contact between citizens and open spaces continues to block the successful movement of open and especially urban spaces. One of the most important parts of this relationship is local citizens’ awareness of open spaces. In this paper key issue in the contacts between citizens and Golestan National Park (GNP) in western part of Iranian capital, Golestan is investigated. In fact, in this paper our objectives are to describe and clarify residents’ attitudes regarding the costs and benefits of GNP, explore the effects of people’s awareness of park management through non- governmental organization.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.254
Teacher spread0.234 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

Explore more

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