Medical Experts and Agnotology in the Fumes Controversy of the Huelva Copper Mines (1888–1890)
Bibliographic record
Abstract
Huelva's copper mines (Spain) have been active for centuries but in the second half of the nineteenth century extractive activities in Riotinto, Tharsis, and other mines in the region were intensified in order to reach world leadership. The method used in these mines for copper extraction from low grade ores generated continuous emissions of fumes that were extremely controversial. The inhabitants had complained about the fumes for decades but as activity intensified so did complaints. The killing of anti-fumes demonstrators in 1888 led to the passing of a Royal Decree banning the open-air roasting of ore and to the drafting of numerous reports on the hazards of the fumes. Major state and provincial medical institutions, as well as renowned hygienists and engineers, took part in the assessment, contributing to a scientific controversy especially rich in content. In my paper I will analyse the production and circulation of knowledge and ignorance about the impact of fumes on public health, as well as the role of medical experts and expertise in the controversy. The analysis will focus on the reports drafted between the 1888 ban and its 1890 repeal, and will show the changing nature of the expert assessment and the numerous paths followed by experts in producing ignorance. The paper will conclude by considering other stakeholders, who may shed some light on the reasons behind the performance of the medical experts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".