AN EXAMINATION OF MULTIDIMENSIONAL POVERTY PROFILE IN LIMPOPO, SOUTH AFRICA: IMPLICATIONS FOR THE USE OF CBMS GENERATED DATA FOR INTEGRATED DEVELOPMENT PLANNING
Bibliographic record
Abstract
This study sought to establish the status of multidimensional poverty in Tzaneen Local Municipality in South Africa by using community-based system (CBMS).The objective was to profile multidimensional poverty on basic services and assess the usability of the generated data by local municipalities for Integrated development planning.Consultative, capacity building and CBMS design approach was adopted in the study.Administered household profile questionnaire was used on 1534 households.Indicators for poverty measurements were generated in partnership with Local municipality and aligned to Statistics South Africa.Data was captured using excel software and imported to CSPro6.3 software for creation of data dictionary and database.Data analysis was done using predetermined indicators and formulas.CBMS-NRDB software was used to generate poverty maps.Community validation approach was used to authenticate study findings.We conclude that CBMS generated multidimensional poverty data and maps can be used for formulating effective Integrated development plans and regular progress monitoring.The information can further be used to assess poverty policy implications at local levels.We recommend that Local municipality use CBMS generated data as a baseline for Integrated development planning and to compliment other national poverty surveys data.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".