Discourse analysis: a study of the social, political context of radioactive pollution effects on Indigenous Communities
Bibliographic record
Abstract
This thesis addresses the effects of radioactive pollution on Indigenous communities, across the world and particularly in Canada. In many cases Indigenous cultural and traditional lifestyle, environment and health of the community are directly impacted due to various sectors of the nuclear industry and its radioactive contamination. My thesis highlights radioactive \ncontamination of Indigenous traditional lands and waters, its impact on health, various instances of historical injustice and displayed experiences of affected Indigenous persons. Additional research is devoted to highlighting the radioactive contamination of Indigenous lands and waters, and in particular I reviewed the impact of tailings on Serpent River First Nation, in Ontario. \n \nIndigenous worldviews and generational wisdom play an important role when it comes to \ncoexistence and conservation of the surrounding environment, policies and role models which act as a guiding principle when it comes to the protection of nature and the wellbeing of future generations. Therefore, this thesis aimed to analyze information and sources with a view to \na) Highlight potential dangers when it comes to radioactive waste in Indigenous \ncommunities; \nb) To promote Indigenous knowledge and worldviews in relation to the surrounding environment and; \nc) To suggest a positive shift in terms of the renewable, waste-free hydrogen fusion process, the very same process that powers stars in the universe including our sun. \n \nA discourse analysis, which consisted of an in-depth analysis of fifteen literature and related sources were oriented to address two key research issues: a) the dangers of radioactive pollution and b) the impact of dangerous tailings in Indigenous communities. An Indigenous methodology (Kovach 2010, Wilson 2008,) overarched the total thesis to ensure that it respected Canada’s Indigenous worldviews. \n \nConsiderable attention is devoted to utilizing a discourse analysis research method for \nanalyzing relevant texts on existing radioactive danger, experiences and the living conditions of Indigenous First Nations as a result of radioactive contamination. This work highlights the importance of implementation and careful consideration of Indigenous worldviews. Key findings call to attention instances of historical injustice addressing the devastating impact on Indigenous cultures, traditional lifestyles, community health, historical injustice, and contamination of the surrounding environment as a result of radioactive pollution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".