Policy as Performance: Indigenisation and Resource Nationalism in Zimbabwe in the 2000s
Bibliographic record
Abstract
In 2008, in the midst of a deepening political-economic crisis, Zimbabwe’s ZANU(PF) government introduced ‘Indigenisation and Economic Empowerment’ as a policy framework to guide the domestication of foreign firms then dominating the formal economy. At the same time, debates around resource nationalism were emerging in the country’s extractives sector, which was booming in the wake of a global surge in minerals prices. The intersection of indigenisation and resource nationalism as two powerful poles of policy-making established the terrain for key extractives-sector reforms in the 2000s under the banner of indigenisation. Drawing on case study evidence from the indigenisation of a large foreign-owned mine and the experiences of community trusts set up to manage mining assets, the paper argues that indigenisation was primarily tailored to accommodate the needs of local elites and foreign-owned mining companies. While indigenisation offered opportunities for elite participation and enhanced ruling party legitimacy, it abjectly failed to transform ownership stakes in the large-scale mining sector. In contrast, local mining communities for whom indigenisation promised a stronger decision-making role were largely marginalised from participation in local mining. Five years after the 2018 scrapping of its enabling laws, indigenisation is seen as having fulfilled a performative political function benefiting the ruling ZANU(PF) at a time of political crisis. The paper concludes that any alternative, inclusive resource nationalist strategy will need to look beyond indigenisation’s narrow framing to be transformational and sustainable.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".