Bibliographic record
Abstract
Emerging out of the horrific spasm of genocide that claimed more than 800,000 of the minority Tutsi ethnic group in 1994, Rwanda has chalked up some spectacular economic performance. Its rate of economic growth has averaged 8% since 2001 and it is among the fastest growing economies in East Africa. Poverty rates have been halved and Rwanda is one of the very few African countries that was able to achieve the United Nations’ Millennium Development Goals (MDGs). It also has the highest representation of women in Parliament.This paper, however, argues that impressive though Rwanda’s economic performance might be, it is not sustainable. First, it is heavily dependent upon foreign aid, making it vulnerable to fluctuations in aid. Second, the Asian Tiger economic model Rwanda copied from Singapore-development under authoritarianism—has failed miserably in postcolonial Africa. No dictator—civilian, military nor rebel—has brought lasting prosperity to any African country. Third, the other types of reform Rwanda needs to sustain its economic achievement—intellectual, political, constitutional and institutional reforms—are petulantly missing. How these reforms are sequenced is also critically important. Focusing only on economic liberalization to the detriment of the other reforms amounts to putting the cart before the horse. Finally and most disconcerting of all, in his understandable zeal to prevent another genocide, Rwanda’s President Paul Kagame is unwittingly re-creating the very conditions that led to the 1994 genocide—a supreme irony.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".