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Record W3108293107 · doi:10.7440/histcrit76.2020.02

El derecho del trabajador al aire puro: contaminación atmosférica, salud y empresas en las cuencas de minerales no ferrosos (1800-1945)

2020· article· en· W3108293107 on OpenAlexaboutno aff
Juan Diego Pérez-Cebada

Bibliographic record

VenueHistoria Crítica · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Objective/context: Social conflicts caused by air pollution gave rise to an intense debate around health in the European and North American non-ferrous mineral basins during the 19th century. Contemporary scholars classified it as a public health problem, since it ultimately affected the entire mining community, encompassing what we know today as environmental and occupational hazards. This article uses selected case studies to analyze institutional regulations, and the scientific and technical solutions that were applied in response to them. However, in the first half of the twentieth century, health problems associated with smelter smoke evolved to become an industrial hygiene issue and were therefore limited to the field of industrial relations. Institutional, scientific and technical factors behind this change are closely related to the role of the social agents concerned, particularly large corporations. Methodology: This research, which is comparative from a temporal and spatial perspective, relies on an analysis of both current literature and contemporary sources. Originality: Air pollution derived from mining activities triggered a process of closely interrelated institutional, scientific and technical innovations. In the late nineteenth century, the ensuing debate became international, spreading from European to American mining basins. Conclusions: The smelter smoke controversy deeply divided the non-ferrous mineral basins of Europe, the United States and Canada, and science provided the foundation for the institutional and technical measures implemented to address it. In that process, particularly since the late nineteenth century, mining companies developed the capacity to successfully adapt the science to their needs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.016
GPT teacher head0.277
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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