Bibliographic record
Abstract
This sentiment analysis entails the examination of commonly held colonial opinions and attitudes that further stigmatize Indigenous Peoples. The YouTube video posted by Global News (2021) chosen for this sentiment analysis speaks on the water crisis as experienced by Indigenous Peoples in which the insufficiencies of the infrastructures the government supposedly implements to provide clean drinking water for Indigenous communities is brought to light. This paper explores the negative sentiments/stereotypes found within the video to illustrate how Indigenous communities experience the water crisis and how it is viewed through an affective lens by settler colonialists. An exploration of the Canadian government’s tokenistic rationalization of their current policies that supposedly attempt to deal with and eradicate the water crisis will attempt to provide a counternarrative to the negative sentiments/stereotypes. Two policies that will be explored include the Indian Act, which placed a lot of power and control in the hands of the federal government in the allocation of funding and resources to Indigenous communities. And the policies that require Indigenous Peoples to go through various challenging and problematic hurdles to acquire funding for water treatment plants in their communities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".