Exploring changes in the lives of the ultra poor: an exploratory study on CFPR/TUP members
Bibliographic record
Abstract
Since January 2002, BRAC has started a new experimental programme for the \nultra poor called, 'Challenging the Frontiers of Poverty Reduction/Targeting the \nUltra Poor' (CFPRffUP). This programme targets the ultra poor who are either \nbypassed or fail to benefit and subsequently drop out from existing development \nprogramme. The programme uses an asset-based approach where physical assets \nare provided to the selected ultra poor women as grants. The intervention strategy \nalso includes health and social development components. The overall idea of the \nprogramme is to strengthen the physical, social and human asset base of the ultra \npoor so that once the grant phase is over, they can attain the foundation for \nsustainable livelihoods, and participate and benefit from mainstream development \nprogrammes. This paper is based on an exploratory study that wanted to better \nunderstand the perceptions of change as defined by the programme members and \nthe underlying factors that explain the changes perceived. The main finding is that \ninitial conditions matter - households that owned homestead land, had other \nsources of income, had adult male labour power and did not suffer from recurrent \nhealth costs did better. The programme encouraged the members to save out of \nthe income accrued from running the TUP enterprise, but the product was \nfocussed on supporting the promotional needs rather than protectional needs. The \ncircumstances of the ultra poor households differ and a sole focus on the .savings \nfor meeting the promotional needs may thus need reconsideration .
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".