Publication:
Machine Learning in Evaluative Synthesis: Lessons from Private Sector Evaluation in the World Bank Group

Simple item page
dc.contributor.authorBravo, Leonardo
dc.contributor.authorHagh, Ariya
dc.contributor.authorJoseph, Roshin
dc.contributor.authorKambe, Hiroaki
dc.contributor.authorXiang, Yuan
dc.contributor.authorVaessen, Jos
dc.date.accessioned2023-07-20T18:39:57Z
dc.date.available2023-07-20T18:39:57Z
dc.date.issued2023-07-20
dc.description.abstractThis resource discusses the use of machine learning (ML) techniques in evaluation research and their potential to automate the process of extracting and classifying large amounts of texts. ML methods can accelerate the process of extracting and classifying content in evaluation research provided that practitioners train the extraction tool properly. In practical terms, such an approach can offer evaluators a powerful analytical tool for a range of evaluative purposes, for example, for better understanding the various determinants of project success, potential challenges to project implementation, and practical lessons for future projects, among others. With the above goal in mind, the paper provides an overview of ML and discusses relevant applications in the field of evaluation. This is sup­ported by the case of the Finance and Private Sector Evaluation Unit of the Independent Evalu­ation Group as an example to illustrate the benefits of ML for text classification in evaluation. The paper concludes by offering a summary of the results of this experiment and a brief discussion of potential next steps.en
dc.identifierhttp://documents.worldbank.org/curated/en/099231406212327198/IDU07e909ace079a6040cf0ad9a00d44b829a79d
dc.identifier.doi10.1596/IEG183052
dc.identifier.urihttps://openknowledge.worldbank.org/handle/10986/40054
dc.languageEnglish
dc.language.isoen_US
dc.publisherWashington, DC: World Bank
dc.relation.ispartofseriesIEG Methods and Evaluation Capacity Development Working Paper Series
dc.rightsCC BY-NC 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/igo
dc.subjectMACHINE LEARNING
dc.subjectINFORMATION EXTRACTION
dc.subjectAUTOMATION
dc.subjectEVALUATION
dc.subjectML
dc.subjectPRIVATE SECTOR
dc.subjectLATENT DIRICHLET ALLOCATION
dc.subjectLDA
dc.titleMachine Learning in Evaluative Synthesisen
dc.title.subtitleLessons from Private Sector Evaluation in the World Bank Groupen
dc.typeWorking Paper
dspace.entity.typePublication
okr.crossref.titleMachine Learning in Evaluative Synthesis: Lessons from Private Sector Evaluation in the World Bank Group
okr.date.disclosure2023-07-20
okr.date.lastmodified2023-06-21T00:00:00Zen
okr.doctypeWorking Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099231406212327198/IDU07e909ace079a6040cf0ad9a00d44b829a79d
okr.guid099231406212327198
okr.identifier.docmidIDU-7e909ace-79a6-40cf-ad9a-0d44b829a79d
okr.identifier.doi10.1596/IEG183052
okr.identifier.doihttp://dx.doi.org/10.1596/IEG183052
okr.identifier.externaldocumentum34099331
okr.identifier.internaldocumentum34099331
okr.identifier.report183052
okr.import.id1199
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099231406212327198/pdf/IDU07e909ace079a6040cf0ad9a00d44b829a79d.pdfen
okr.region.geographicalWorld
okr.topicInformation and Communication Technologies::ICT Applications
okr.topicInformation and Communication Technologies::Knowledge Management
okr.unitMethods Advisory Function (IEGMA)
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
IDU07e909ace079a6040cf0ad9a00d44b829a79d.pdf
Size:
864.57 KB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
IDU07e909ace079a6040cf0ad9a00d44b829a79d.txt
Size:
91.94 KB
Format:
Plain Text
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Plain Text
Description:
Download
Collections
IEG Independent Evaluations & Annual Reviews