Good Governance and Development in Botswana – The Democracy Conundrum
Bibliographic record
Abstract
Abstract Unlike many of its African neighbours, Botswana achieved levels of socio-economic development in spite of its abundant mineral wealth. Botswana’s effective management of its mineral resources also aided in its avoidance of the resource curse and corresponding weak institutions. The contribution of Botswana’s mineral wealth to its development best characterizes the country as a “resource-rich developmental state.” However, the correlation between democratic principles and institutions to Botswana’s developmental success was unclear. This paper examines the connection between democracy and development in Botswana by relying on the “thin” versus “thick” spectrum of democratic institutions expounded by Mariana Prado, Mario Schapiro, and Diogo Coutinho. The paper argues that Botswana’s institutions are not democratically “thick”; therefore, democracy and “good” governance, as its conceived neoliberally, do not explain Botswana’s development outcomes. Instead, this paper contends that David Trubek, Diogo Coutinho, and Mario Schapiro’s “legal functionalities” framework, which credits the success of development policies to four roles the legal system could play: (i) safeguarding flexibility, (ii) stimulating orchestration, (iii) framing synergy, and (iv) ensuring legitimacy, is better suited to explain the success of Botswana’s resource-rich developmental state.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 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".