Towards a Culturally Sustainable Environmental Impact Assessment: The Protection of Ainu Cultural Heritage in the Saru River Cultural Impact Assessment, Japan
Bibliographic record
Abstract
Abstract Culturally sustainable environmental impact assessment (EIA) requires consideration of the impact of development on local people's cultural activities, including holding ceremonies, collecting resources, and learning skills, which are fundamental essences of Indigenous rights. While culturally sustainable EIA has become a common practice when a development project involves an Indigenous community, it is still argued that Indigenous cultural heritage is not adequately protected. This is due to the fact that Indigenous people do not always keep power in the post‐approval stage of EIA, or the lack of practical measures to minimise the impact of development projects on Indigenous cultural heritage and to enhance the possibility of reaching a consensus among stakeholders. The Cultural Impact Assessment of the Saru River Region in Japan was the first investigation of a site to preserve an ethnic minority culture, with regard to a dam construction. In the second phase of the assessment project, research staff members, some of whom are of Ainu ethnicity, suggested alternative ceremony sites and conducted experimental transplants to protect the local cultural activities. The long‐term investigation by research staff, in fact, influenced the direction of the dam construction. The developer agreed not to proceed with the construction until measures were taken to minimise the impact on cultural activities that would satisfy residents in the construction area. While still early to conclude that Indigenous participation in this assessment project has been successful, Indigenous participation has clearly enhanced the possibility of reaching a consensus. The project should be considered with other published EIA reports, in demonstrating a return from investing in EIA with Indigenous participation, with a practical means for realising Indigenous rights.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".