Social Policy Trends: Low Income Entry and Exit Rates
Bibliographic record
Abstract
Each year, some people fall into low income, while others rise out of it.How large are these movements?By observing changes in people's income from one year to the next, Statistics Canada records the number of individuals who move into and out of low income each year.The percentage of people who in the previous year were defined as having low income, but now have income greater than this threshold, defines the low-income exit rate.These people have had an increase in earnings sufficient to escape low-income status.Similarly, Statistics Canada calculates the percentage of people who have experienced a fall in income sufficient to cause them to now be identified as having low income.The percentage of people whose income was previously above the measure of low income, but whose earnings are now below that measure, defines the low-income entry rate.The figure shows how low-income entry and exit rates have changed over the period 1992 to 2019.The data is for Canada and might look very different if examined by province or even city.In the last year reported in the figure, 2.6% of people who were not in low-income in 2018 moved into low-income in 2019.This was equal to 628,465 people.Similarly, 38.6% of people who were in low-income in 2018 had moved out of low-income in 2019.This was equal to 986,765 people.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".