An ‘Effective’ Involvement of Indigenous People in Environmental Impact Assessment: the cultural impact assessment of the Saru River Region, Japan
Bibliographic record
Abstract
The Cultural Impact Assessment of the Saru River Region represents the first time that a site investigation was implemented in Japan in order to preserve an ethnic culture in relation to the construction of a dam. One of the project's basic concepts was to get local residents, especially those of Ainu ethnicity, to participate in the investigation. Existing case studies of environmental impact assessment have argued that the assessment has failed to sufficiently involve Indigenous people in its process and has largely failed to incorporate Indigenous knowledge, cultural values, and voices into its processes and outcomes. Also, intangible aspects of Indigenous cultural heritage have not been protected. In the Cultural Impact Assessment of the Saru River Region, the Final Report was released in 2006 and significantly included the 3 year investigation of input by local residents. In this sense, this assessment succeeded in effectively involving Indigenous people in its process and in reflecting their cultural values in its results. The more important issue is, however, how these results were included in the final outcomes. If Indigenous people have no power over final decision making, their involvement is not effective. This paper analyses the significance and unresolved problems involved in this overall assessment process.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 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".