Causal Attributions and the Significance of Self-Efficacy in Predicting Solutions to Poverty
Bibliographic record
Abstract
Abstract Attributions about the causes of poverty in America and perceptions concerning viable solutions to poverty in America should be related. People who believe that the causes of poverty lie within the individual (i.e., make an internal attribution) should be supportive of the individual taking responsibility for his or her economic situation as the solution to poverty. In contrast, people who believe that the causes of poverty lie outside the control of the individual (i.e., make an external attribution) should perceive governmental assistance programs as a viable solution to poverty. In addition, it is possible that an individual's level of self-efficacy moderates the relationship between attributions as to the causes of poverty and proposals about solutions to poverty. Data used for this investigation were obtained from the Great Lakes Poll, a large-scale telephone survey of residents in the Midwest of the United States and the province of Ontario, Canada. Statistical analysis of the data via OLS regression found no support for the idea that self-efficacy serves as a moderator. However, support was found for the belief that an individual's attributions towards the causes of poverty are consistent with that person's proposed solutions to poverty. Thus, internal attributions towards causation of poverty predict individual solutions to poverty; conversely, external attributions towards causation predict external solutions for poverty.
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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) | 0.001 | 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".