Bibliographic record
Abstract
∗Magdalena Benza provided assistance to process GPS referenced data and to construct ‘true ’ travel time variables. We thank seminar participants at the department of Economics and the Social Statistics seminar at McGill University, GRADE, and conference participants at the 44e Congrès de la Sociéte ́ Cana-dienne des Sciences Économiques (Québec) and at the 75 Years of Development Research International Colloquium (Cornell) for their insightful comments Studies in the microeconometric literature increasingly utilize distance to or time to reach markets or social services as determinants of economic issues. These studies typically use self-reported measures from survey data, often characterized by non-classical measurement error. This paper is the first validation study of access to markets data. New and unique data from Peru allow comparison of self-reported variables with scientifically calculated variables. We investigate the determinants of the deviation between imputed and self-reported data and show that it is non-classical and dependent on observable socio-economic variables. Our results suggest that studies using self-reported measures of access may be estimating biased effects. 1
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.251 | 0.096 |
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".