Public Attributions for Poverty in Canada*
Bibliographic record
Abstract
Les auteurs de cet article décrivent les caractéristiques sociales de la pauvreté en utilisant des données d'interviews téléphoniques effectuées en 2002 au moyen d'un échantillon aléatoire d'adultes sélectionnéà partir de huit voisinages à Toronto et Edmonton, enrichi par des données d'interviews. Une régression logistique multivariée a été utilisée afin de prédire l'attribution des caractéristiques structurelles, individualistes, intergénérationnelles et fatalistes à la pauvreté, en se servant de variables démographiques et de la variable exposition à la pauvreté. Les participants étaient plus susceptibles d'expliquer la pauvreté par des causes structurelles et moins susceptibles de favoriser une explication individualiste. Le revenu a été associé négativement à des déterminants individualistes, fatalistes et à une des causes structurelles, et lié positivement au facteur intergénérationnel. This paper describes public attributions for poverty using data from telephone interviews conducted in 2002 with a random sample of adults from eight neighbourhoods in Toronto and Edmonton, supplemented with interview data. Multivariate logistic regression was used to predict support for structural, individualistic, intergenerational and fatalistic attributions for poverty by demographic and exposure-to-poverty variables. Participants were most likely to attribute poverty to structural causes and least likely to favour individualistic attributions. Income was negatively associated with individualistic, fatalistic and one of the structural attributions, and positively related to the intergenerational attribution.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".