"A search for understanding: A proposal for researching Native homelessness in Northern Manitoba"
Bibliographic record
Abstract
Poverty has been a prevalent issue throughout world history. \nDespite the United Nation's opinion that we have the best quality of life, Canada cannot say that \n it is the exception to this rule. Many people live in abject poverty here. Without an \nofficial poverty line to help determine the level of poverty, estimating the actual number of poor \nis a contentious issue. Most official reports base their findings on Statistics Canada's Low \nIncome Cut Offs (LICO's). For example, the National Council of Welfare estimate that in 1997 just \nover five million Canadians, or 17.2% of the population, were living in poverty (Silver,2000). \nSuch findings generally underestimate the number of poor Canadians as they usually do not \ninclude data on Aboriginal people living on reserves, residents of the Yukon, Nunavut, \nand North West Territories, and people who live in institutions. The costs surrounding \npoverty are enormous. For example, population health studies show a strong correlation between \npoverty and people's health. They argue that poverty contributes to many of our social ills, \naffecting individuals, families, communities and society as a whole (for example, see Layton, \n2000). Though there are discrepancies in the actual rates and effects of poverty, poverty rate and \npopulation health studies have one major commonality, they indicate that poverty is increasing \n(Ross, Scott, & Smith, \n2000). At the margins of this growing trend, one often finds the homeless.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.027 | 0.022 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.010 | 0.012 |
| 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".