How can a Theory of Change framework be applied to short-term international volunteering?
Bibliographic record
Abstract
Short-term international volunteering has become enormously popular among individuals from high-income countries who travel to low-income countries to offer support on initiatives often related to health and development. However, their impact on global development is questionable, particularly when volunteer skills are not matched to local needs, or when teams operate outside the local health system. Furthermore, the impact of these volunteer programs is rarely evaluated. Theory of Change is a framework for program design meant to facilitate measurable social change. We propose that a Theory of Change framework, appropriately deployed in the design and conduct of short-term international volunteerism, could help improve volunteer efforts by identifying problems and clearly defining goals, designing and implementing effective strategies, and evaluating the real impacts these have on identified concerns.
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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 | 0.068 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".