Nin-gikino’amaagoz – I am a student
Bibliographic record
Abstract
According to data collected from the 2016 Canada Census, there were 34 million Canadians, 2.1 million Indigenous people, and 1.5 million First Nations people. Of the total Canadian population, 28.6 million had obtained a high school diploma or equivalate certificate, while 1.2 million Indigenous people obtained the same level of education, but only 227,945 First Nations people living on-reserve graduated from high school or obtained an equivalent certificate in Canada. Therefore, at the time of the last census in Canada, only 7% of the total Canadian population, First Nations students living on-reserve graduating from high school while close to 80% of the total population who where non-Indigenous where graduating from high school in the same year and in the same country. In Manitoba, high school graduation rates for First Nations students are 39.9% lower than their non-Indigenous counterparts. There are many contributing factors to why First Nations high school graduation rates are so low. Many of these issues have stemmed from racist and assimilative polices legislated through different variations of the Indian Act. These policies have contributed to a wide array of socioeconomic issues among Indigenous people, both living on and off-reserve. As we move on to the next part of this journey, I will highlight the context and histories of these policies. You will learn that assimilation, racism, and genocide are deeply rooted in Canadian history, policy, and legislation. Which plays a significant role in educational gaps and the mistrust of authority and government.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.021 |
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".