Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Our aim in this paper is to show how recent developments in the theory and methods of poverty measurement can be applied to provide more accurate descriptions of poverty trends to the typical consumers of these statistics—policy analysts, policy‐makers and their critics. Since Amartya Sen's (1976) classic critique of the “headcount” approach to poverty measurement, considerable progress has been made in constructing axiomatically‐driven measures of “poverty intensity.” These measures have had little influence outside the small world of experts who devised them largely because their mathematical representation has made their meaning obscure to potential users. We focus on the Sen‐Shorrocks‐Thon (SST) index and its elaboration by Osberg and Xu which provides the information contained in the index in a format that is easily accessible within traditional categories of poverty analysis. The SST index and its decomposition provide an analytical framework for discussing the underlying components of aggregate trends that allows for unambiguous answers to the usual policy‐related questions concerning the components of change as well as their magnitude and direction.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it