MétaCan
Menu
Back to cohort
Record W4410239885 · doi:10.1080/00309230.2025.2496206

Rationalism and empiricism: contrasting approaches to drawing as education reform in the late nineteenth century

2025· article· en· W4410239885 on OpenAlexaffabout
Patrice Milewski

Bibliographic record

VenuePaedagogica Historica · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Education Studies Worldwide
Canadian institutionsLaurentian University
Fundersnot available
KeywordsEmpiricismRationalismSociologyEpistemologySocial sciencePhilosophy

Abstract

fetched live from OpenAlex

Distinct from studies that discuss art education and the formation of a visual culture, this article compares the different uses and ends which drawing served in Ontario and the United States in last two decades of the nineteenth century. Drawing in Ontario and the US represented differing systems of knowledge and truth claims that were vying for prominence as a foundation for education in the late nineteenth century: Froebelian rationalist philosophy in Ontario and empirical positivist science in the US. This article identifies the networks associated with the different uses that drawing served in education reform. A common interest in Froebelian philosophy and kindergarten classes linked Ontario educator James L. Hughes with educators in Boston. This provided access to the innovations in the teaching of drawing that were being made by Walter Smith. Smith was a key figure in the process of establishing and promoting the importance of drawing, industrial drawing and design in Britain, United States, Ontario and Brazil in the last half of the nineteenth century. Another network was centered around G. Stanley Hall and the empirical positivist science of education that underpinned large-scale studies of children’s drawings. The methods that Hall and his collaborators employed to study children’s drawings were critical to the making of child studies in America. This article contends that neither of the examples of the uses of drawing analysed in this article served a liberatory purpose.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.115
GPT teacher head0.360
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venuePaedagogica HistoricaSame topicHistorical Education Studies WorldwideFrench-language works237,207