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Record W3085467031 · doi:10.3390/soc10030069

Social Protection Implementation Issues in Ethiopia: Client Households’ Perceived Enablers and Constrainers of the Productive Safety Net Program

2020· article· en· W3085467031 on OpenAlex
Melisew Dejene Lemma, Logan Cochrane

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocieties · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsCarleton University
Fundersnot available
KeywordsSafety netBusinessContext (archaeology)Order (exchange)Service delivery frameworkProgram Design LanguageService (business)Public relationsMarketingProcess managementComputer sciencePolitical scienceFinanceEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

Social protection programs need to be suited to the specific context within which they are implemented. To minimize barriers and constraints in implementation, program design needs to integrate and respond to the views of client households and potential beneficiaries, ideally with on-going feedback mechanisms to better respond both to constrainers and to enablers. In order to provide evidence regarding constrainers and enablers in Ethiopia’s safety net program, we conducted a household survey to assess policy-backed efforts for social protection service delivery. This paper outlines client households’ perceived enablers and constrainers regarding the implementing of the Productive Safety Net Program, Africa’s second largest safety net. The findings suggest that client households have identified enablers and constrainers from their lived experience that could be used as a feedback mechanism and as input for future program design. The findings could foster better outcomes in program implementation.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.328
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it