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Record W2746284887 · doi:10.3138/chr.4142

Early Educational Exclusion: “Idiotic” and “Imbecilic” Children, Their Families, and the Toronto Public School System, 1914–50

2017· article· en· W2746284887 on OpenAlexvenueaboutno aff
Jason A. Ellis

Bibliographic record

VenueCanadian Historical Review · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsnot available
Fundersnot available
KeywordsEugenicsHarmSpecial educationMental deficiencySociologyClass (philosophy)CriminologyPsychologyGender studiesPolitical sciencePedagogySocial psychologyLawPsychiatry

Abstract

fetched live from OpenAlex

This article uses pupil records and patient files from Toronto, along with other sources, to examine the interconnected rise of special education classes and the complete exclusion from public schools of children labelled with a form of mental deficiency referred to as “idiocy” or “imbecility” or later represented by an intelligence quotient below fifty. It was the first provincial special education laws that also enabled schools to legally exclude some children not just from the new special classes but also from public schooling altogether. Drawing on eugenics and the ideas of mental deficiency experts, school officials justified exclusion by claiming that some types of disabled children were “ineducable” – they could not learn or their presence in schools would harm others' learning. The article looks at student experiences to show how exclusion curtailed their schooling. It also examines how families responded to the exclusion. Families were historical actors, despite the limitations imposed on them by their circumstances, by the policies and prevailing attitudes about class, gender, and single mothers, and by the nature of their family member's disability. The article contributes to “the new disability history,” a growing subfield.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.377
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
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 head0.052
GPT teacher head0.248
Teacher spread0.196 · 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
GenreReview

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

Citations8
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Historical ReviewSame topicHistorical Studies on Reproduction, Gender, Health, and Societal ChangesFrench-language works237,207