AN EVALUATION METHODOLOGY FOR MEASURING THE LONG-TERM IMPACT OF FAMILY STRENGTHENING AND ALTERNATIVE CHILD CARE SERVICES: THE CASE OF SOS CHILDREN’S VILLAGES
Bibliographic record
Abstract
Until recently, SOS Children’s Villages International, like many organisations in the social sector, lacked a rigorous and systematic approach to gauging the long-term impact of their services. With this in mind, SOS Children’s Villages International developed a social impact evaluation methodology in 2014 to measure the long-term effects of its services on children and their families and communities, as well as the social return on investment. This evaluation methodology has been tested and applied to similar service types across 15 low-, middle-, and high-income countries worldwide. The findings are regularly consolidated, in order to derive trends and learnings for the global organisation and to inform strategy and policy. The present article will discuss the evaluation methodology and the related limitations. Conclusions regarding the validity of the methodology will be offered in terms of the measurement of social service impact and the way forward.
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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 | 0.259 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".