David A. Green and Jonathan R. Kesselman, Eds. Dimensions of Inequality in Canada
Bibliographic record
Abstract
David A. Green and Jonathan R. Kesselman, eds. Dimensions of Inequality in Canada. Vancouver: UBC Press, 2007. 477 pp. Index. $29.95 sc. Dimensions of Inequality in one of three edited volumes that came about from Equality, Security, and Community project. The project was conducted over a six-year period with purpose of explaining and improving distribution of in Canada (ii). The book provides an overall assessment of in Canada. The most common income. A survey of income over 1990s using three data sources has come to different conclusions (due to difference in way income reported). Survey data stable levels of whereas more reliable tax and census data point to rapid increases in income during 1990s (15). Measurements of in terms of consumption have been similar to that of survey data. However, earnings mobility is dynamic complement of inequality (101). The probability of staying in same earnings category higher for women than for men. The probabilities of moving up distribution are generally higher for men, whereas probabilities of moving down one or more earnings categories are higher for women (108). Mobility also much greater toward bottom of earnings distribution than at top end where high-skill workers enjoy much more stable earnings patterns (123). Unfortunately, movements within earnings distribution follow a pattern in which rich tend to stay rich and tend to stay poor (14-15). Examining employment levels another method of measuring inequality. Disturbing trends are evident in differences in working time of adults. According to data from International Labour Organization, in twenty-year period between 1980 to 2000 average actual working time per adult (ages fifteen to sixty-four) rose in United States by 234 hours to 1,476 while falling in Germany by 170 hours to 973 (155). Moreover, relatively in United States work significantly harder and still end up with less income than their European counterparts (i.e., France, Germany, Sweden and United Kingdom). Leisure time (an indicator of economic well-being) has a direct impact on an individual's money income level. However, because of substantial variation in leisure time between countries, of level of money income likely understate degree of differences in of economic well-being (179). Compared to Europe, the distribution of economic among Canadians even more unequal then money income comparisons alone would indicate (179). Ethnic and visible minorities face prevalent economic and health inequalities. All groups of Aboriginals are disadvantaged not only in wages and salaries but also self-report highest cases of major chronic diseases. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".