Bibliographic record
Abstract
This chapter explores how representations of indigenous peoples on the Internet and other media are contextualized according to an outsider worldview, and that much of the information about indigenous peoples accessed through virtual media lack the original context in which to position the information. This means that the information is completely distanced from the indigenous peoples whom the information is purported to represent. This is problematic when representations of indigenous peoples are defined by dominant discourses which promote bias and reinforce stereotypes. With the increase of technology and the race to globalization, symbols are being reconstructed and redefined to connect and create a global identity for indigenous peoples. The consequences of this further the current practices of erasing and reconstructing indigenous history, language, culture and tradition through control and commodification of representations and symbols. This removal from history and community ensures continued silencing of indigenous voices. Although these misrepresentations continue to frame the discourse for indigenous peoples in Canada, it is time for indigenous peoples to reclaim and resist these representations and for outsiders to stop creating social narratives for indigenous peoples which support western hegemony.
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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 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".