ATTRIBUTIONS OF RESPONSIBILITY FOR POVERTY AMONG LEBANESE AND PORTUGUESE UNIVERSITY STUDENTS: A CROSS-CULTURAL COMPARISON
Bibliographic record
Abstract
This study examines poverty attitudes among Portuguese and Lebanese students (n=372) along Feagin's fatalistic, individualistic, and structuralistic dimensions. Results show that class and nationality are important variables for predicting the causes of poverty in cross-cultural terms. Lebanese students had higher agreements on the fatalistic dimension of poverty than did Portuguese. Significant differences were found between the middle-class Portuguese and Lebanese students on the individualistic and fatalistic dimensions. Middle-class Lebanese students were significantly more fatalistic than their Portuguese counterparts. Furthermore, middle-class Lebanese students documented greater individualistic interpretations of poverty than did Portuguese. MANCOVA test, which used class crossed with nationality on the poverty dimensions, and gender as a covariate did not yield significant differences between means. Wilks' Lambda regression coefficient showed a significant interaction between-class and nationality on the fatalistic dimension. Although the results portray different scores of poverty from those in previous studies, Lebanese students' structuralistic attributions are explained by the present economic and social crises of their country which transcend a strong orientation of system blame. Recommendations are offered for future crosscultural research on poverty.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".