Nuclear fuel waste and aboriginal concerns : Canada's nuclear fuel waste management concept public hearings--a content analysis
Bibliographic record
Abstract
This thesis examined Aboriginal views on nuclear fuel waste management in Canada and assessed the concerns and issues Aboriginal people are likely to voice at future interactions and deliberations in the next siting phase. A content analysis method was used to examine the entire public record produced during the 1996/1997 Federal Environmental Assessment Review Panel hearings held on the Environmental Impact Statement for the concept of geological disposal of nuclear fuel waste. The content analysis indicated that Aboriginal peoples have continued to express opposition to the geologic disposal concept with intensity and consistency as demonstrated by measures of issue frequency and number of lines expended on each issue in the testimony. Further, the study indicated that native views remained consistent when compared with earlier scoping hearings in 1991, and that their positions were substantively and culturally different than non-native responses to the concept. In addition, two case studies were examined where natives in North America have been confronted with, and expressed views on, nuclear fuel waste storage or disposal, in order to further demonstrate the consistency of native views. The study found that Aboriginal responses have likely influenced the consideration of alternative disposal concepts in the long-standing Canadian nuclear waste management 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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".