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Record W2323497221 · doi:10.1386/eta.11.2.277_1

Zema and Peter Haworth: A double-jointed biography from the history of art education

2015· article· en· W2323497221 on OpenAlexafffundabout
Dustin Garnet

Bibliographic record

VenueInternational Journal of Education through Art · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMemoirBiographyThe artsIdeal (ethics)Visual artsEmbodied cognitionArt historySociologyLife writingArtHistoryPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

Abstract In this double-jointed biography, the author narrates the tandem tale of two parallel lives that directly contributed to the art community in Toronto, Canada, for generations. Each of these two great Canadians dedicated over 30 years to the instruction of visual art education at Central Technical School (CTS) and their lasting legacy has produced some of Canada’s most celebrated artists. Zema (Bobs) Haworth and Peter Haworth immigrated to Canada from England in the early 1920s and developed into successful artists, advocates, educators and socialites whose lives were inextricably intertwined with the larger institutional history of the Art Department at CTS. Tracing Zema’s and Peter’s lives through archival research, published memoirs and interviews, photos, and other personal documents, the author explores how this couple embodied the ideal of the artist-teacher and contributed to the visual arts at both the local and national levels.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.525
Threshold uncertainty score0.955

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0230.017
Scholarly communication0.0070.003
Open science0.0010.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.063
GPT teacher head0.293
Teacher spread0.230 · 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.

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

Citations4
Published2015
Admission routes3
Has abstractyes

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