Mental Models of Poverty in Developing Nations: A Causal Mapping Analysis Using a Canada-Philippines Contrast
Bibliographic record
Abstract
Causal mapping was used to compare poverty activists and non-activists from Canada and the Philippines (N = 80) in terms of their beliefs about the causes of poverty in developing nations. The causal maps varied as a function of both activist status and country of residence. Activists included more external societal causes in their maps than non-activists, whereas non-activists included more individualistic and internal societal causes. In terms of map structure, Filipino activists included significantly more causal links in their maps than members of the other three groups. A cluster analysis on distance ratios, an index of dissimilarity among the maps, produced three clusters dominated by Filipino non-activists, Canadian non-activists, and Filipino activists, respectively, and a fourth cluster that included a heterogeneous mix of respondents from all four groups. Implications for public education, the effective coordination of antipoverty interventions, and methodological issues related to causal mapping are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".