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
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.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".