Corrigendum to “Measuring the poverty reduction effects of adopting agricultural technologies in rural Ethiopia: Findings from an endogenous switching regression approach”
Bibliographic record
Abstract
In the original published version of this article, Muhammad IbrahimShah would like to remove the affiliation with Department of Resource Economics and Environmental Sociology (REES), University of Alberta, Edmonton.The correction version should be as following: Mesele BelayZegeyea, Getamesay Bekele Mesheshaa,b, Muhammad IbrahimShahcaDepartment of Economics, Debre Berhan University, P.O. Box 445, Debre Berhan, EthiopiabDepartment of Development Economics, Ethiopian Civil Service University, Addis Ababa, EthiopiacAlma Mater Department of Economics, University of Dhaka, Bangladesh The authors apologize for the errors. Both the HTML and PDF versions of the article have been updated to correct the errors. Measuring the poverty reduction effects of adopting agricultural technologies in rural Ethiopia: findings from an endogenous switching regression approachZegeye et al.HeliyonMay 17, 2022In BriefAgricultural technology; Rural Ethiopia; Poverty; Multinomial logit; Endogenous switching model. Full-Text PDF Open Access
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.011 |
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".