SenseMaker® as a monitoring and evaluation tool to provide new insights on gender-based violence programs and services in Lebanon
Bibliographic record
Abstract
Monitoring and evaluation (M&E) of gender-based violence (GBV) programs is challenging in humanitarian settings. To address these challenges, we used SenseMaker® as a mixed methods M&E tool for GBV services and programs in Lebanon. Over a three-month period in 2018, a total of 198 self-interpreted stories were collected from women and girls accessing GBV programs from six service providers across five locations. The resultant mixed-methods analysis provided holistic and nuanced insights on how perceived benefits differed by type of GBV program, how motivations for accessing programs differed by location, and how feelings while accessing programs differed by participant nationality. SenseMaker reinforced the intersectionality between events leading up to the accessed services, the experiences of accessing the services, and subsequent outcomes as a result of having accessed the services, helping to contextualize the findings within the broader experiences of participating women and girls. Limited literacy and technology skills among participants proved to be a challenge and future work should investigate how technology might facilitate use of the tool among participants with lower literacy and technology skills in addition to exploring the feasibility and added value of SenseMaker as an M&E tool in acute humanitarian settings.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".