Export Prospects and Problems Africa Region
Bibliographic record
Abstract
Although Kenya was once viewed as being among the African countries with the most favorable growth prospects, the last two decades witnessed significant declines in many measures of economic performance and social standards. As a result, Kenya’s share of world trade is now less than one-half its average level in the early-1980s. How can Kenya halt, and then reverse, these negative trends? While the answer to this question has multiple dimensions, trade policy certainly can play an important positive role. This report examines reasons why Kenya and other African trade did not provide the “engine of growth ” that it did elsewhere. During the last quarter century many Sub-Saharan African countries non-oil exports either declined in absolute terms, or expanded at a slower pace than world trade. Evidence suggests African countries, including Kenya, experienced serious supply constraints that limited their ability to capitalize on opportunities of international production sharing in foreign markets. Inappropriate governance policies, and a general unfavorable commercial environment, were largely responsible for Africa’s supply problems. In addition, a decomposition of recent trade changes into supply and demand factors shows that, with two important exceptions (cut flowers and apparel exports) Kenya experienced a
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".