Engaging elite support for the poorest? BRAC's experience with the ultra poor programme (TUP working paper -3)
Bibliographic record
Abstract
This paper describes and draws lessons from the experience of engaging village elite in \nsupport of the ultra poor through the Gram Shahayak Committees (GSC), as part of \nBRAC's CFPR/TUP programme. The paper addresses the following questions: under \nwhat conditions can elite become engaged in support of interventions for the ultra poor? \nWhat are the risks and benefits of engaging elite in antipoverty programmes? After \ndescribing the origins and motivations behind BRAC's Specially Targeted Ultra Poor \n(TUP) programme, the paper goes on to explain how an important lesson from the \nprogramme as it evolved included the need for on-site, village-based protection and \nsupport for TUP participants and their newly-acquired assets. The paper goes on to \nexplore some of the early impacts of the GSCs which were formed to fill this need, and to \nassess the motivations and factors underlying their effectiveness and success. The paper \nconcludes with a brief discussion of the lessons from the experience, including their \nimplications for assumptions that dominate scholarship and programmes relating· to the \nrural politics of poverty in Bangladesh.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".