Measuring energy poverty : focusing on what matters : OPHI working paper no. 42
Bibliographic record
Abstract
The provision of modern energy services is recognised as a critical foundation for sustainable\ndevelopment, and is central to the everyday lives of people. Effective policies to dramatically expand\nmodern energy access need to be grounded in a robust information-base. Metrics that can be used for\ncomparative purposes and to track progress towards targets therefore represent an essential support tool.\nThis paper reviews the relevant literature, and discusses the adequacy and applicability of existing\ninstruments to measure energy poverty. Drawing on those insights, it proposes a new composite index\nto measure energy poverty. Both the associated methodology and initial results for several African\ncountries are discussed. Whereas most existing indicators and composite indices focus on assessing the\naccess to energy, or the degree of development related to energy, our new index – the Multidimensional\nEnergy Poverty Index (MEPI) – focuses on the deprivation of access to modern energy services. It\ncaptures both the incidence and intensity of energy poverty, and provides a new tool to support policymaking.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".