Contextualizing Narratives of Economic Growth and Navigating Problematic Data: Economic Trends in Ethiopia (1999–2017)
Bibliographic record
Abstract
There are common narratives about economic growth in Ethiopia. We analyze four common narratives, namely, that (1) the economy is transforming from agriculture to industry, (2) that national economic growth has been rapid and sustained, (3) that Ethiopia’s economy is largely agricultural, and (4) that there is a looming debt crisis, largely due to lending from China. In many instances, the justification for these narratives is based upon single years or specific data points. We examine these narratives over the long term, to assess if they are supported by available macroeconomic data. In doing so, we encountered significant issues with data quality and consistency. This article presents the available datasets from 1999 to 2017 and concludes that the commonly made claims about the Ethiopian economy are sometimes accurate, sometimes incomplete, and other times inaccurate. We call for greater attention to primary data, and primary datasets, as opposed to relying upon secondary summaries, single years, or specific data points to make generalized claims.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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; 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".