Summary Of: Social Assistance Use in Canada: National and Provincial Trends in Incidence, Entry and Exit
Bibliographic record
Abstract
This paper summarizes findings from the research paper entitled Social Assistance Use in Canada: National and Provincial Trends in Incidence, Entry and Exit. For many Canadian families, Social Assistance (SA) usage reflects near-destitution and an exclusion from the social and economic mainstream. For children, it can represent a critical period of disadvantage with potentially lasting effects. While committed to SA, governments worry about cost. Thus, when SA participation rose during the recession of the early 1990s, virtually all provinces instituted changes to reduce SA dependency. Eligibility rules were made tighter, benefit levels cut, and 'snitch' lines introduced. Following these changes, and the economic recovery post-1995, the number of SA-dependent individuals dropped from 3.1 million to under 2 million by 2000, while benefits received fell from $14.3b in 1994 to $10.4b in 2001 (current dollars). This paper maps the cycle of SA dependency, focusing on empirical records of SA entry, exit, and annual participation rates, placing these in the economic and policy context of the 1990s. The paper begins with a description of the database used, sample selection and editing procedures, the unit of analysis, a definition of SA participation, and the measure of entry and exit from SA. It then turns to the economic and policy backdrop of the 1990s, before showing results at the national and provincial levels. We conclude with a summary of main findings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".