Conceptualization of Principal Variable Selection for Computing the Water Poverty Index
Bibliographic record
Abstract
This paper develops a conceptual framework resulting in an alternative method for selecting principal variables for computing a Water Poverty Index (WPI) at the national scale. To this end, a principal components variable selection strategy is applied to the 2002 data obtained for 147 countries on five key components- resource, access, capacity, use, and environment- compiled by Keele University. The results suggest that only three components (i.e., access, capacity, and environment) with different weights (i.e., the highest weight to capacity and the lowest weight to environment) be used for WPI calculation. It also turns out that a simpler index, based on two components (i.e., capacity and environment) with equal weights, can be computed with little information loss. The new WPIs, which are shown to correlate well with two well-known socioeconomic indicators, could help government, non-government, and business organizations set priorities for communities and countries of greatest resource needs.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".