Into the deep: science, politics and law in conflicts over marine dumping of mine waste
Bibliographic record
Abstract
Abstract National and international deliberations about whether the global mining industry should be permitted to use the earth's seas as mine waste dumps are not decided on the basis of independent scientific data from past or existing sites. Nor is a critical lack of scientific knowledge about the deep sea environment and about the long‐term consequences of marine dumping triggering the precautionary principle for proposed projects. Rather, governments make decisions on submarine tailings disposal (STD) on a mine‐by‐mine basis, in a context of contested scientific claims, considerable political and economic pressure, and, sometimes, contravening or amending existing legislation to allow a project to proceed. These decisions provoke complex social processes that commonly involve: local opposition; political conflict; regulatory and legal challenges; powerful industry lobbies; international financial institutions, and resource‐hungry states. STD projects engender controversy in developing countries, such as Papua New Guinea (Divecha 2002; Shearman 2002a), Indonesia (Edinger 2012; Glynn 2002) and the Philippines (Coumans et al . 2002; Coumans and MaCEC 2002), but also in Canada (Cultural Survival 1982) and Norway (Kvassness et al . 2009). This paper examines cases drawn from the Philippines, Canada and Papua New Guinea to illustrate common themes that arise in conflicts surrounding STD mine projects.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.098 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".