Public Policy and Economic Rights in Ghana and Uganda
Bibliographic record
Abstract
GIVING MACROECONOMIC REFORM A CHANCE This chapter advocates more serious attention by human rights scholars to macro-economic reform. Macroeconomic reform in some poor countries, including the two on which this chapter focuses, has been necessary during the last twenty-five years for economic growth. In turn, economic growth is necessary, but not sufficient, for economic rights. Many countries in sub-Saharan Africa have undergone significant macroeconomic reform since the 1980s. Structural adjustment programs (SAPs) advocated by the International Monetary Fund (IMF) and the World Bank (WB) led this reform. Typical characteristics of SAPs in Africa are fiscal austerity, trade liberalization, privatization of state-owned enterprises (SOEs), abolition of state marketing boards, export-led agricultural reform, retrenchment of civil servants, and currency devaluation. There is widespread concern that SAPs have failed Africa (Ibhawoh 1999, 158–67). Aware of this concern, many human rights scholars have concluded that macroeconomic reform and SAPs are always detrimental to economic human rights, as specified in the International Covenant on Economic, Social, and Cultural Rights. In the case of sub-Saharan Africa, the economic rights most commonly referred to in criticism of macroeconomic reform are those to food, health, education, and access to clean water. We suggest that blanket dismissal of macroeconomic reform as a path to economic human rights may be too hasty. Although macroeconomic reform is not necessarily beneficial to economic rights, it may in some cases contribute to their realization. Macroeconomic reform can release blocked productive capacities. The release of productive capacities helps citizens to earn their own livelihood.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".