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Record W2795741337

Diana Bayley: A Grandmama's Tale

2007· article· fr· W2795741337 on OpenAlexaboutno aff
Elizabeth Waterston

Bibliographic record

VenueCanadian Children's Literature / Littérature canadienne pour la jeunesse · 2007
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsBiographyHumanitiesArtWifeHistoryArt historyPhilosophyTheology
DOInot available

Abstract

fetched live from OpenAlex

Resume: L'on croit que Madame Diana Bayley (? - 1868), auteur de Henry; or, the Juvenile Traveller (1836), est la premiere personne au Canada a avoir ecrit des œuvres pour la jeunesse. Epouse d'un administrateur britannique, elle s'est etablie au Bas-Canada en 1832 et a publie, de 1833 a 1834, des recits dans The Montreal Museum . Elle est la mere ou la belle-mere de Frederick W.N. Bayley, prolifique auteur pour la jeunesse de la seconde moitie du dix-neuvieme siecle; apres son retour en Angleterre, elle a poursuivi sa carriere de romanciere. L'article explique son absence du Dictionnaire biographique du Canada. Summary: Mrs. Diana Bayley (b.?, d. 1868), author of Henry; or, the Juvenile Traveller (1836), is believed to be the first resident of Canada to write for children. The wife of an English administrator, Assistant Commissary-General Henry Addington Bayley, Diana Bayley came to Lower Canada in 1832. She wrote for a juvenile periodical, The Montreal Museum , from 1833 to 1834, when it ceased publication. She is either the mother or the step-mother of Frederick W.N. Bayley, later a prolific writer for children. Mrs. Bayley wrote other novels after returning to England. This article explains how she got dropped from the Dictionary of Canadian Biography , due to uncertainty over her death date.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.742
Threshold uncertainty score0.514

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.002
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0780.021

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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