EXPLORING MULTIDIMENSIONAL PERSPECTIVE OF POVERTY AMONG THE RURAL PANGALS IN MANIPUR: A CASE STUDY
Bibliographic record
Abstract
This paper analyses poverty among the rural Pangals in Manipur on a Multidimensional perspective. For the analysis, Borayangbi Gram Panchayat where Pangal community settle in large number is selected. By utilizing a field survey data conducted during the first quarter of 2019 the Multidimensional Poverty of Borayangbi Gram Panchayat is estimated. Borayangbi is a remote village located in the southern part of Imphal Valley under Moirang Sub-Division in Bishnupur District, Manipur. The village is worthwhile to study its level of poverty and deprivation as there are limited studies in this area. Multidimensional Poverty Index captures the simultaneous deprivations of each person in different households. The methodology used in the study is developed by Alkire and Foster and involves three dimensions: health, education and living standard. Additional indicators are also used to suit the study of the area concerned. This methodology enhances the better understanding of poverty and deprivation of the concern village. A stratified random sampling technique was used to conduct the survey of 100 households in the village. In the study, it is found that the largest contribution of deprivation is the dimension of living standard. People in the village experience maximum deprivation in the indicators of cooking fuels and safe drinking water. The results and information can be used to design policy perspective of the village and help in targeting poverty alleviation program. KEYWORDS: Multidimensional Poverty Index, Alkire Foster Method, Borayangbi, Pangals
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".