Bibliographic record
Abstract
This essay critiques the Western colonial ideology of exploiting natural resources for profit and its detrimental impact on ecosystems and Indigenous communities. It advocates for recognizing the rights of non-human entities, exemplified by the Magpie River in Quebec, which was granted legal personhood. I contrast the Western profit-driven worldview with Indigenous ontologies, which emphasize the intrinsic value of nature and its interconnectedness with human and otherthan-human entities. The paper explores how Indigenous perspectives challenge the commodification of land and water, advocating for a more harmonious and sustainable coexistence. I draw on the concept of “Pluriverse Worlding”, which recognizes multiple ways of understanding the world and rejects the Western “One-World World” mentality that imposes anthropocentric dominance. By adopting Indigenous ontologies, the paper argues, we can protect ecosystems and counter the profit-driven mindset. The paper also critiques the Western legal system’s failure to adequately protect the environment, emphasizing the need to incorporate Indigenous worldviews into environmental protection laws. This shift requires embracing Indigenous knowledge systems, fostering dialogue between Indigenous and Western perspectives. Through legal, philosophical, and practical strategies, the paper emphasizes the importance of safeguarding ecosystems and other-than-human entities for the benefit of the planet and humanity.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".