A rocky road to evidence: Evaluating literacy programmes using a trust-based approach in a context of fragility
Bibliographic record
Abstract
Background: Evaluation in contexts of fragility and violence has recently received attention because of the increased complexity of conducting such evaluations. The use of digital tools has been advocated for conducting these evaluations, but with limited results. Objectives: This article presents an in-depth analysis of combining digital tools with in-person activities to build trust and develop the type of human interaction required to improve the quality of evaluation design and implementation in the context of insecurity, fragility and violence. Method: Data collection was conducted both offline and online. Enumerators collected data through face-to-face individual interviews, and statistical analysis was performed using STATA software version 17. Results: The objectives of data collection were achieved at 99%, notwithstanding the challenging security environment. Several factors contributed to this achievement, notably our methodological framework based on trust building, digitisation and iterative programming. Despite this commendable performance, the overall efficiency was found to be 63%, indicating a potential for a 37% reduction in data collection time. Conclusion: The proposed trust-based approach has been successfully tested to enhance the quality of baseline studies and establish conditions for the success of other phases of evaluations. Contribution: This case study serves as an evidence to what we call a trust-based approach to the use of digital tools in evaluation processes. We contend that the effectiveness of digital tools in enhancing the quality of evaluation design, especially in the context of fragility and violence, hinges on their integration with face-to-face activities, trust-based human interaction and careful timing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".