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Record W4375956029 · doi:10.1080/00026980.2023.2201743

Amateur Science and Innovation in Fireworks in Nineteenth-Century Europe

2023· article· en· W4375956029 on OpenAlexfundno aff
David Garrioch

Bibliographic record

VenueAmbix · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
FundersAustralian Research CouncilMonash UniversityYork University
KeywordsFireworksAmateurHistoryField (mathematics)Visual artsAestheticsSociologyArtArchaeology

Abstract

fetched live from OpenAlex

The categories of "amateur" and "professional" remain central in studies on the sociology of nineteenth-century science. This article joins a growing body of literature that points out the complicated and intersecting connections between these two groups and how blurred the boundaries could be. This study focuses on pyrotechny, the art of fireworks, a field of far more obvious importance in the nineteenth century than it is today. Firework displays were mounted by artisan firework makers, who by the end of the century had become industrialists, and by military specialists, usually artillerymen. They had also become a common amateur pursuit. Across the nineteenth century, the art was transformed by the introduction of new materials, and the key discoveries were the work of enthusiasts who did not seek to profit financially from their discoveries. In this sense, they too were amateurs, although some had a scientific education. This article asks how they were able to make such major contributions to the field, and it situates them within networks that often crossed the boundaries between those who made fireworks for a living, or who studied them in military contexts, and those who were simple enthusiasts.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0110.018
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.028
GPT teacher head0.232
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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
Published2023
Admission routes1
Has abstractyes

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