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Record W2022342170 · doi:10.1017/s0269889707001470

Educating Idiots: Utopian Ideals and Practical Organization Regarding Idiocy inside Nineteenth-Century French Asylums

2007· article· en· W2022342170 on OpenAlexaff
Sofie Lachapelle

Bibliographic record

VenueScience in Context · 2007
Typearticle
Languageen
FieldPsychology
TopicPsychoanalysis and Psychopathology Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIdiotArgument (complex analysis)Tone (literature)Period (music)Space (punctuation)LawSociologyAestheticsPolitical sciencePsychologyMedicinePsychiatryLiteraturePhilosophyArt

Abstract

fetched live from OpenAlex

Argument Throughout the nineteenth century, French alienists reflected on the nature of idiocy, on its causes and possible treatments. Central to this reflection was the question of education. Was it possible to teach a child idiot to develop physically, intellectually, and morally? Schools were established, wards were rearranged, and educational methods were suggested. The extent to which all of this succeeded is hard to assess. The optimistic tone of educational treatises was never reflected in the life in the asylum. By the end of the century, the dichotomy between theoretical ideals and practical reality came to a halt as both methodological treatises on education and pleas for funding ceased. Soon, idiots left the wards and their schools for new classes within the common school system. While the former practice had proved successful in improving the patients' abilities, it was claimed that it had failed to bring about the social integration for which alienists had once hoped. This final period marked a rupture in the treatment of idiocy, both in terms of space and organization from asylums to schools and from alienists to psychologists.

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)0.005
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.041
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.399
Teacher spread0.364 · 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

Citations5
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueScience in ContextSame topicPsychoanalysis and Psychopathology ResearchFrench-language works237,207