'Mining money': science, legal technologies & working the borderlands of global finance
Bibliographic record
Abstract
This paper explores the roles of science and market devices in the commodification of ‘nature’ and the configuration of flows of speculative capital. It focuses on mineral prospecting and the market for shares in ‘junior’ mining companies. In recent years these companies have expanded the reach of their exploration activities overseas, taking advantage of innovations in exploration methodologies and the liberalisation of fiscal and property regimes in ‘emerging’ mineral rich developing countries. Recent literature has explored how the reconfiguration of notions of ‘risk’ has structured the uneven distribution of rents. It is increasingly evident that neoliberal framing of environmental, political, social and economic risks has set in motion overflows that multinational mining capital had not bargained for (e.g. nationalisation, violence and political resistance). However, the role of ‘geological risk’ in animating flows of mining finance is often assumed as a ‘technical’ given. Yet geological knowledge claims, translated locally, designed to travel globally, assemble heterogeneous elements within distanciated regimes of metrology, valuation and commodity production. This paper explores how knowledge of nature is enrolled within systems of property relations, focusing on the genealogy of the knowledge practices that animate contemporary circuits of speculative mining finance. It argues that the financing of mineral prospecting mobilises pragmatic and situated forms of knowledge rather than actuarially driven calculations that promise predictability. A Canadian public enquiry struck in the wake of scandal associated with Bre-X’s prospecting activities in Indonesia is used to glean insights into the ways in which the construction of a system of public warrant to underpin financial speculation is predicated upon particular subjectivities and the outworking of everyday practices and struggles over ‘value’. Reflection on practical investments in processes of standardisation, rituals of verification and systems of accreditation reveal much about how the materiality of things shape the ways in which regional and global financial circuits are integrated, selectively transforming existing social relations and forms of knowledge production.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".