Testing the Validity of the Ontario Deprivation Index
Bibliographic record
Abstract
What is a deprivation index? Since the late 1800s, many different ways of measuring poverty have been developed. Beginning in the 1960s and continuing until the last decade, Canada generally used the Low Income Cut-Offs (LICOs) to measure poverty based on what an average family spent on food, shelter and clothing. The LICOs have now been supplemented by two additional measures, which are becoming more widely used. One is the Low-Income Measure (LIM) based on a percentage of median income – 40 or 50 percent of median income, called the LIM40 or LIM50 respectively. The other is the Market Basket Measure (MBM) which attempts to reflect the cost of a basket of goods and services deemed necessary in a modern country such as Canada. All of these ways of measuring poverty have differing advantages and disadvantages, which we will discuss in detail in a future paper, but what they all have in common is the use of income to measure poverty. Each implicitly claims that a family below a certain amount of income is likely to be experiencing a poverty-level standard of living. Yet we know from everyday experience that households may require differing amounts of income. Households have not only income but wealth – positively in the form of assets or negatively in the form of debts. Households have many special
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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 | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".