Bibliographic record
Abstract
As international financial institutions pushed African governments to withdraw from the ownership and management of businesses in the 1980s and 1990s, across the continent governments involved in mining enterprises sold off state-owned assets to private investors. Through the boom and bust cycles of the first decades of the twenty-first century, multinationals headquartered in Europe, Canada, and Australia, Chinese state-owned enterprises, Indian and Brazilian companies, and a range of smaller companies from South Africa and beyond invested billions in existing mines and new greenfield sites. Reflecting on the wave of privatization and foreign investment, social science scholarship from the 2000s often framed mining as enclaved production, emphasizing how companies disentangle themselves from the surrounding society and shed the social project previously associated with parastatal companies (Ferguson 2005). Later, attention turned to the work that companies perform to produce and securitise the enclave – through processes that inevitably create political and social entanglements (Appel 2012; Hönke 2010). The focus on the enclave rightly emphasizes the power of mining companies but can elide how the entanglement of mining companies in different contexts produces a range of spaces and infrastructures, social formations and networks, while reorienting others. It also overlooks how different socio-political contexts shape mining operations. The politics of mining involves a wide range of actors and institutions including contractor companies, trade unions, regulatory bodies, courts, NGOs, and ethnic and community associations. Moreover, the micropolitics of mining plays out amidst wider socio-political changes brought by liberalization and often interacts with reemerging politics of nationalism or government efforts to reclaim or reconfigure regulatory power.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.125 | 0.040 |
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".