Bibliographic record
Abstract
This paper explores mine waste that originates from resource extraction by specifically focusing on waste rock, tailings, dust and material culture from the resource extraction industry. By drawing on examples from fieldwork, archives, local media commentary and limited interviews from two iron-mining regions in Arctic Norway and sub-Arctic Canada, this paper follows mine waste as it routinely transgresses attempts to be managed. Mine waste spills out of its prescribed sinks, it oscillates between being considered waste to heritage to potentially valuable commodity, and it blurs the boundaries between spaces dedicated for mining and for non-mining. In following these trends, the paper calls for attentiveness to the ambiguous materiality of mine waste and how heterogeneity and excess circumscribe attempts at easy characterisation and management of the ubiquitous wastes that come to dominate mining regions. As such, archaeological approaches to studying mine waste can illustrate how mine waste becomes the default, lived-with condition of life in regions dominated by ongoing mining operations.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.428 | 0.262 |
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".