Working Paper 40 The Benefits of Rural Roads: Enhancing Income Opportunities for the Rural Poor
Bibliographic record
Abstract
IDRC of Canada, supported the initial stage of this research. Substantial progress and writing of this study was done while Javier Escobal was appointed as a Guggenheim fellow to work on the links between rural producers and markets between 2001 and 2002. We are grateful to the authorization provided by the World Bank and the Rural Roads Program (PCR) of the Ministry of Transport for the usage of the survey on which this study is based. We would also like to acknowledge the comments to earlier drafts of this study provided by Arie Kuyvenhoven, Ruerd Ruben and Nico Heerink, from the Development Economics Group, University of Wageningen. Of course, we are responsible for any remaining errors and for the analysis contained in this study. GRADE´s working papers have the purpose of disseminating in a timely fashion the results of research undertaken in the institution. In accordance with the stated mission and objectives of the institution, their purpose is to generate debate among members of the scientific community in order to enrich the final product of the research process, such that it may provide policy makers with solid technical input. The opinions and recommendations expressed in this document are those of the authors and do not necessarily represent the views of GRADE or of the institutions that support it.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".