High income families (IS 944 A6) Winter 1994 (Vol. 6, No. 4) Article No. 6
Bibliographic record
Abstract
The characteristics of families with low incomes are often studied in detail in order to assist in the development of policies and programs that will improve their economic position. In contrast, there is generally very little analysis of the characteristics of families at the top of the income ladder, possibly because of a lack of data on this small group. The Census of Canada is the only survey with a large enough sample base to allow an analysis of many of the characteristics of very small segments of the Canadian population. A study entitled Characteristics of High Income Families was published in 1986 based on data collected in the 1981 Census. It examined the main features of families that constituted the top one percent of income distribution, and noted that the "impact of high income families on the rest of society is considerable. The incomes of these families are generally well above their consumption needs. Thus, their savings become a very important source of funds required for investment and stimuli for economic activity. [These] families often hold responsible positions in the socio-economic structure. Thus, they may be involved in decisions which have a direct impact on a large segment of society." Using information from the 1991 Census, this article examines income and other characteristics of families that formed the top percentile in 1990 (See Data source and definitions).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.118 | 0.016 |
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".