Bibliographic record
Abstract
Poverty in Canada is on the rise, particularly among certain groups. While in developing countries poverty may affect much of the population, in a more developed country such as Canada it is largely restricted to specific groups. Such groups are often excluded from full participation in our social and economic institutions. There are many factors behind this lack of wealth and opportunity; addressing the phenomenon of poverty can be a complicated matter. Government demographer and lecturer Raghubar Sharma provides the first concise discussion of the specific groups that are affected by poverty, including the elderly, ethnic poverty, child poverty, and the working poor. Chapters focus on these groups and explore the circumstances behind their exclusion. Sharma also looks into a larger trend behind the rise of poverty: a massive economic transformation akin to the Industrial Revolution of the early 1700s has been underway since the 1980s. This phenomenon of globalization is eliminating labour-intensive jobs and polarizing the job market into high-skill, high-paying jobs on one hand and low-skill, low-paying jobs on the other. In these circumstances, the less-qualified, the disadvantaged, and the discriminated are unable to find decent jobs, and hence are destined to a life of poverty. As the world becomes a global village, there is an urgent need to understand poverty in Canada. Sharma's book, one of the first to consider the wide range of factors behind poverty in Canada, will be accessible to students as well as general readers interested in the growing reality of wealth inequality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.023 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".