The slow rejection of mercury in Yukon’s small-scale gold mining industry
Bibliographic record
Abstract
Mercury technologies in small-scale gold mining (SSM) operations have been slowly rejected in Yukon, Canada. We analyse fieldwork data collected over 4.5 months between 2020–2023 from 32 semi-structured interviews, 20 placer mine visits, and participatory observation notes. Using diffusion of Innovation theory (DoI) we identify prior conditions (i-iv) required to escalate the rejection of mercury as a processing technology more widely. Data relevant to the slow rejection of mercury technologies were thematically coded around technological, governmental and societal shifts. We find that the (i) previous practice involving mercury technologies shifted through time in response to (ii) felt needs/problems (initially technological but later including health, environment, and community needs/problems). The shifts were addressed by the (iii) innovativeness of the community; enabled through formalization channels and possibly by access to resources such as electricity. Eventually mercury-centred practice was eclipsed by chemical free processes which led to the evolution of new (iv) norms of a social system. Globally, where artisanal scale mining (ASM) and SSM industries continue to be reliant on mercury, consideration of prior conditions could help identify context-specific opportunities for mercury mitigation and draw attention to the need for mercury recycling programs to redress legacy mercury.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".