New Framework for Multidimensional Environmental Well-being for Sustainable Development
Bibliographic record
Abstract
ABSTRACT This study seeks to develop a Multidimensional Environmental Well-Being Criteria (MEWC) using the censored achievement approach, specifically the Alkire-Foster counting methodology. Its overall objective is to use MEWC to measure the multiple environmental deprivations. It provides a comparison of empirical evidence of the existing monetary and multidimensional poverty index (MPI) measures and the proposed MEWC. The MEWC is composed of three indicators: crop productivity, level of perceived environmental knowledge, and agricultural and extension education. For empirical analysis, the authros randomly sample 376 households from 20 communities in the Abuakwa North and South municipalities in Ghana. The empirical analysis is divided in two parts: part one provides information on existing well-being parameters such as income/poverty metrics (upper and lower poverty lines) and multidimensional poverty index (MPI); and the second part analyzes the fieldwork element related to the three identified indicators of environmental wellbeing and its implications for sustainable development for the selected regions in Ghana, and how it could be applied to other municipalities/districts and other countries. The incidence and severity of environmental deprivation, robustness checks, and statistical inference are also analyzed. Results show that MEWC for the two municipalities in Ghana is 0.46 or 46%, which represents the proportion of deprivations that the multi-environmentally poor in the sampled communities experience, as a share of the multi-environmental deprivations that would be experienced if all persons were multi-environmentally poor and deprived in all the dimensions of multiple environmental poverty. From a policy perspective, MEWC could be used to track the sustainable development goals targeting the poor and to design policies and strategies that address the inter-twined environmental deprivations of the poor. The MEWC could also contribute to addressing the technical problems emanating from the multidimensionality of the concept of poverty.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".