Implementation of a SenseMaker® research project among Syrian refugees in Lebanon
Bibliographic record
Abstract
The Syrian conflict has displaced over 1.2 million Syrians into Lebanon. As a result of displacement, some Syrian families are turning to child marriage as a coping mechanism. The prevalence of early marriage has reportedly increased and the average age of marriage decreased during the crisis. The aim of the project was to understand the underlying factors contributing to child marriage among Syrian refugees in Lebanon using Cognitive Edge's SenseMaker®. This manuscript explores the process of implementing this novel research tool in a humanitarian setting. Twelve interviewers conducted SenseMaker® interviews with married and unmarried Syrian girls, Syrian parents, as well as married and unmarried men. Participants were asked to share a story about the lives of Syrian girls in Lebanon and to self-interpret the narratives by answering follow-up questions in relation to the story provided. Data collection occurred across three locations: Beirut, Beqaa, and Tripoli. In total 1422 narratives from 1346 unique participants were collected over 7 weeks. Data collection using SenseMaker® was efficient, capable of electronically capturing a large volume of quantitative and qualitative data. SenseMaker® limitations from a research perspective include lack of skip logic and inability to adjust font size on the iOS app. SenseMaker® was an efficient mixed methods data collection tool that was well received by participants in a refugee setting in Lebanon. The utility of SenseMaker® for research could be improved by adding skip logic and by being able to adjust font size on the iOS app.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".