Using a mixed method approach to discuss the intersectionalities of class, education, and gender in natural disasters for rural vulnerable communities in Pakistan
Bibliographic record
Abstract
During the floods of 2014, Pakistan lost 267 human lives. 2.5 million people were displaced, 129,880 houses were fully or partially destroyed, and over 1 million acres of cropland and 250,000 farmers were affected, which resulted in the loss of cash crops and standing food. Using Intersectionality Theory, the current study examines the effects of income, education, land ownership, land type, disaster type, gender, and disability on the loss of agricultural crops, controlling for respondents’ age. Secondary data was used for this study from a 2012 baseline survey of disaster risk reduction, conducted by a nongovernment organization in District Muzaffargarh, Punjab, Pakistan. Logistic regression was used to analyze the data. Results indicated that education of household head, high income, and land ownership decreased the likelihood of losing agricultural crops, whereas floods, women-headed households, and disabled family members increased the likelihood of losing agricultural crops. Keywords: intersectionality; natural disasters; rural vulnerable communities Résumé Durant les inondations de 2014, le Pakistan a perdu 267 vies humaines. 2.5 millions de personnes furent déplacées, 129 880 maisons furent totalement ou partiellement détruites, et plus d'un million d'acre de terres cultivées et 250 000 fermiers furent affectés, ce qui a entraîné la perte des cultures commerciales et des disponibilités alimentaires. En utilisant la théorie de l'intersectionnalité, la présente étude examine les effets du revenu, de l'éducation, de la propriété foncière, du type de sol, du type de catastrophe, le genre, et l'incapacité qui a suivi la perte des terres agricoles, en considérant l'âge des répondants. Des données secondaires ont été utilisées pour cette étude, comme base de référence de réduction des risques de catastrophe, et conduites par une organisation non gouvernementale dans le district de Muzaffargarh, au Pendjab, au Pakistan. La régression logistique a été utilisée pour analyser les données. Les résultats ont indiqué que l'éducation du chef de famille, un haut revenu, et une propriété foncière diminuaient la probabilité de perdre des terres agricoles, tandis que les inondations, les femmes chef de famille, et des membres de famille invalides augmentaient la probabilité de perdre des terres cultivables.
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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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".