Comparability of<scp>GDP</scp>estimates in<scp>S</scp>ub‐<scp>S</scp>aharan<scp>A</scp>frica: The effect of Revisions in Sources and Methods Since Structural Adjustment
Bibliographic record
Abstract
The unreliability ofAfrican income estimates was highlighted whenGhana announced thatGDPestimates were revised upwards by 60.3 percent inNovember 2010. Similar revisions are to be expected in other countries. Many statistical offices are currently using outdated base years. It is argued that with the current uneven application of methods and poor availability of data, any ranking of countries according toGDPlevels is misleading. The paper emphasizes the challenges for “data users” in light of these revisions.GDPdata are disseminated through international organizations, but without any detailed data descriptions. It is argued that many statistical offices inSub‐SaharanAfrica struggled to recover from the structural adjustment period, and the offices have not had the capacity to handle other challenges such as providing data to monitor the Millennium Development Goals. Currently, neither data users nor data producers are getting the assistance they need.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".