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Record W274082995 · doi:10.5860/choice.45-0118

Crossing the divide: representations of deafness in biography

2007· article· en· W274082995 on OpenAlexaboutno aff

Bibliographic record

VenueChoice Reviews Online · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsBiographyHistoryGenealogyArt history

Abstract

fetched live from OpenAlex

This remarkable volume examines the process by which three deaf, French biographers from the 19th and 20th centuries attempted to cross the cultural divide between deaf and hearing worlds through their work. The very different approach taken by each writer sheds light on determining at what point an individual s assimilation into society endanger his or her sense of personal identity. Author Hartig begins by assessing the publications of Jean-Ferdinand Berthier (1803-1886). Berthier wrote about Auguste Bebian, Abbe de l Epee, and Abbe Sicard, all of whom taught at the National Institute for the Deaf in Paris. Although Berthier presented compelling portraits of their entire lives, he paid special attention to their political and social activism, his main interest. Yvonne Pitrois (1880-1937) pursued her particular interest in the lives of deaf-blind people. Her biography of Helen Keller focused on her subject s destiny in conjunction with her unique relationship with Anne Sullivan. Corinne Rocheleau-Rouleau (1881-1963) recounted the historical circumstances that led French-Canadian pioneer women to leave France. The true value of her work resides in her portraits of these pioneer women: maternal women, warriors, religious women, with an emphasis on their lives and the choices they made. Crossing the Divide reveals clearly the passion these biographers shared for narrating the lives of those they viewed as heroes of an emerging French deaf community. All three used the genre of biography not only as a means of external exploration but also as a way to plumb their innermost selves and to resolve ambivalence about their own deafness.

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)0.002
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: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.055
GPT teacher head0.388
Teacher spread0.334 · 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

Citations18
Published2007
Admission routes1
Has abstractyes

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