The effects of outdated data and outliers on Kenya’s 2019 Global Food Security Index score and rank
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While composite indicators are considered robust in measuring food security, outdated data and outliers challenge their reliability. Outdated data can occur when national databases are not frequently updated while outliers are extremely small or large values in a study. Outdated data could be referred to as missing current data in composite indicators used for annual benchmarking exercises, where data must be frequently updated. Besides hindering useful information within an index, outdated data could also result in outliers in a database, especially when the outdated or missing current data are imputed by estimation. Studies that have assessed the robustness of composite indicators highlight that outdated data and outliers could bias results, thereby hindering an index's reliability. However, depending on the methods used when constructing a composite indicator, some methods can be considered robust even with the presence of outliers in a data point. Outdated national data could hinder countries from tracking the progress of international, national or regional commitments, such as the Sustainable Development Goals, while outliers could act as an unintended benchmark. This study assessed the impacts of outdated data and outliers on Kenya's scores and rankings in the Global Food Security Index (GFSI). The study objective was achieved by assessing Kenya's performance in the 2019 GFSI result before and after removing outliers from the GFSI data points and updating Kenya's outdated indicators. Winsorisation was used to remove the outliers from the GFSI database, while the Spearman correlation and Paired t-tests were used to test for the statistical significance of the outdated data and outliers. The study revealed that while Kenya's 2019 GFSI database did not have outliers, outliers in other countries' data points impacted Kenya's score and rank. For example, the winsorisation of outliers for other countries reduced Kenya's 2019 overall GFSI score by six points. Moreover, thirteen indicators in Kenya's 2019 GFSI database were found to be outdated. However, despite Kenya's score improving from updating the outdated data, the impact was minimal to increase the GFSI's mean score for all countries. That is, updating Kenya's outdated indicators was found not to differ significantly from zero. The study concluded that Kenya's score and rank in the 2019 GFSI were affected by the outdated data in Kenya's database and outliers in other countries' data. The study, therefore, recommended that Kenya should update its national database and allow open access to the national data while the GFSI should identify and remove outliers from the data points.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it