AN ANALYTICAL BASIS FOR BOTSWANA'S DIAMOND- ENCLAVE SUSTAINABLE ECONOMIC GROWTH AND WELLBEING: LESSONS FOR NIGERIA
Bibliographic record
Abstract
The performance of natural resource-rich nations in Europe (Norway, USA, Canada and Russia) and in the Middle East (Saudi Arabia, Iran, Kuwait, United Arab Emirates, Libya, Qatar and Algeria) suggests natural resources can be a decisive advantage to sustainable economic growth and development. The paradox of ‘resource curse’ is more of a sub-Saharan African phenomenon than a general rule. Even within the Africa context, there are exceptions to this malaise. One such exception is Botswana, a leading exporter of diamonds, which – unlike most natural resource-rich countries in sub-Saharan Africa – has achieved an impressive economic growth and performance since independence. Over the last two decades, the economy of Botswana grew at about 7.8% on average, the highest growth rate across the whole sub-Saharan African region. Over 40% of this growth is driven by the mining sector, which provides up to 80% of total export earnings for the country (Fofack, 2009:1–2). This study holds that if an oil extractive rich economy like Nigeria curb corruption and put oil resource windfalls to good use, her economy and society would hardly face social and economic unrest. Since the late 1980s, revenue from oil production continues to rise in Nigeria while political leadership is corrupt and majority of the population very poor (Social Development Integrated Centre, 2012, Fagbadebo, 2007, 030-031). This is also the case with Azerbaijan, Kazakhstan, and Turkmenistan in the Caspian basin; as well as the Southeast Asian countries of Cambodia, East Timor, Myanmar, and Vietnam (Ross, 2008:3; Eifert, et. al, 2003). It is within the foregoing context that this study reviews the secret behind Botswana’s sustainable economic growth and wellbeing, which are largely attributed to good governance (Leigh, et al., 2012, Kiiza, et al., 2011). This
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".