Lay understandings of the effects of poverty: a Canadian perspective
Bibliographic record
Abstract
Although there is a large body of research dedicated to exploring public attributions for poverty, considerably less attention has been directed to public understandings about the effects of poverty. In this paper, we describe lay understandings of the effects of poverty and the factors that potentially influence these perceptions, using data from a telephone survey conducted in 2002 on a random sample (n=1671) of adults from eight neighbourhoods in two large Canadian cities (Edmonton and Toronto). These data were supplemented with interview data obtained from 153 people living in these same neighbourhoods. Multivariate linear and logistic regressions were used to determine the effects of basic demographic variables, exposure to poverty and attribution for poverty on three dependent variables relating to the effects of poverty: participation in community life, the relationship between poverty and health and challenges facing low-income people. Ninety-one per cent of survey respondents agreed that poverty is linked to health, while 68% agreed that low-income people are less likely to participate in community life. Affordable housing was deemed especially difficult to obtain by 96%, but other resources (obtaining healthy food, giving children a good start in life, and engaging in healthy behaviours) were also viewed as challenging by at least 70% of respondents. The regression models revealed that when controlling for demographics, exposure to poverty explained some of the variance in recognising the effects of poverty. Media exposure positively influenced recognition of the poverty-health link, and attending formal talks was strongly related to understanding challenges of poverty. Attributions for poverty accounted for slightly more of the variance in the dependent variables. Specifically, structural and sociocultural attributions predicted greater recognition of the effects of poverty, in particular the challenges of poverty, while individualistic attributions predicted less recognition. Older and female respondents were more likely to acknowledge the effects of poverty. Income was positively associated with recognition of the poverty-health link, negatively associated with understanding the challenges of low-income people, and unrelated to perceptions of the negative effect of poverty on participation in community life.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".