Effects of Cultural Landscape Conservation and Utilization Activities on Residential Value Cognition -Shiga Prefecture Takashima City as a case-
Bibliographic record
Abstract
In Japan, the law for the conservation of cultural landscapes was established in 2005. 43 areas are selected as “Important Cultural Landscapes” by the Minister of Education. Cultural Landscapes are strongly influenced by the local people's lives and livelihoods. Therefore, residents’ participation is essential to promote the conservation of cultural landscapes. However, the residents’ perception of cultural landscape value is not so high, and that cause the problems to proceed the activity of landscape conservation and local revitalization. Therefore, this study aim to clarify the effects and the problems of conservation activity from the view point of residents’ participation and recognition toward Cultural Landscapes. We did Hearing survey with the municipal office and community organizations in Takashima City. As results, it is needed for many residential people to share the image of landscape creation and its need at the stage of before making the conservation plan of important cultural landscape. Moreover, through the half-forcibly participation of conservation activity organized by traditional council, residential people can foster awareness toward the conservation activity.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".