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Record W2345635713

지역임업 및 지역 산림 관리 활성화 방안(1/2차연도)

2014· article· ko· W2345635713 on OpenAlexaboutno aff
민경택, 석현덕, 구자춘

Bibliographic record

Venue한국농촌경제연구원 기타연구보고서 · 2014
Typearticle
Languageko
FieldAgricultural and Biological Sciences
TopicEcology and Conservation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)SustainabilityBusinessGeographyAgroforestryEconomic growthEnvironmental planningEnvironmental protectionNatural resource economicsEnvironmental resource managementEconomicsEnvironmental scienceEcology
DOInot available

Abstract

fetched live from OpenAlex

In order to rehabilitate degraded forests, Korea government had implemented various policies such as tree planting campaign, promoting energy conversion and preventing slash/burn cultivation. Due to these strong policies, it was possible to green degraded forests in a short period. However, local people have been isolated from forests in the process. It caused various problems such as degrading the ecosystem services of forest (e.g. biodiversity), collapse of mountain village society and increasing burden of government budget. Consequently, the sustainability of forest is threatened. Thus, it is needed to convert Korean forest policy from government led- to private sector and local community led-. Community based forest management (hereafter CBFM), in which local people participate, have been emerging as an alternative way for recovering the broken relationship between human and natural resources. This study aims to find appropriate ways of implementing CBFM in Korea. We collected the various cases of CBFM in not only developing countries but also developed countries. In order to gather the information efficiently, we carried out oversea research and held an international seminar on CBFM. In order to evaluate the preferences of Koreans to CBFM, we conducted a survey with 1,000 urban dwellers and face to face interview with 22 representatives of forest village from two counties. We reviewed the cases of CBFM from 11 countries (USA, Canada, Mexico, Sweden, England, Japan, Bhutan, Nepal, Philippines, Ghana and Ethiopia). By analyzing these cases, we can derive two important messages. (1) In most countries, CBFM has played an important role in enhancing the value of forests as well as in stabilizing the livelihood of local people. Thus, Korean government also needs to apply CBFM to current forest policy. (2) When we apply CBFM, various conditions of forest and local people should be considered. Because the type of CBFM is dependent on its historical background, legal and institutional system, level of public awareness and so on. For example, the aims of CBFM in Western Europe and Japan are oriented to recreational and educational services. On the other hand, stabilizing livelihood is the most important purpose of CBFM in Bhutan and Nepal. The major player of CBFM in Western Europe and Japan was volunteers. In North America, Indian tribes have played an important role in community. The survey results of urban dwellers show that about 62% of respondents are willing to participate in urban forest management. The activities that they can easily participate in were preferred such as greening their own garden and picking up waste in trails. It is preferable that government and citizens share their role in forest management. Even though most local people recognize the necessity of their participation in forest management, the willingness of local people to participate was relatively low. It can be interpreted as due to the lack of resources such as labor, capital and skill. Policy challenges that can be derived from these results are as follows: (1) the governance system which local people easily participate in forest management should be established; (2) the supporting system which helps the aged in rural areas should be set up; (3) government should lower the barrier preventing local people from utilizing not only non-timber products but also various ecosystem services in national forests; (4) the governance should develop their own business model to ensure stable income of local people; (5) various programs that urban dwellers can participate in should be prepared by local government.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.003

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 head0.026
GPT teacher head0.219
Teacher spread0.193 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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