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Record W2817258020 · doi:10.3138/9781442617735

Unarrested Archives: Case Studies in Twentieth-Century Canadian Women's Authorship

2014· book· en· W2817258020 on OpenAlexaboutno aff
Linda M. Morra

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2014
Typebook
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryGenealogy

Abstract

fetched live from OpenAlex

Calling upon the archives of Canadian writers E. Pauline Johnson (1861-1913), Emily Carr (1871-1945), Sheila Watson (1909-1998), Jane Rule (1931-2007), and M. NourbeSe Philip (1947- ), Linda M. Morra explores the ways in which women's archives have been uniquely conceptualized in scholarly discourses and shaped by socio-political forces. She also provides a framework for understanding the creative interventions these women staged to protect their records. Through these case studies, Morra traces the influence of institutions such as national archives and libraries, and regulatory bodies such as border service agencies on the creation, presentation, and preservation of women's archival collections.The deliberate selection of the five literary case studies allows Morra to examine changing archival practices over time, shifting definitions of nationhood and national literary history, varying treatments of race, gender, and sexual orientation, and the ways in which these forces affected the writers' reputations and their archives. Morra also productively reflects on Jacques Derrida's Archive Fever and postmodern feminist scholarship related to the relationship between writing, authority, and identity to showcase the ways in which female writers in Canada have represented themselves and their careers in the public record

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.911

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.015
Science and technology studies0.0590.024
Scholarly communication0.0130.003
Open science0.0060.007
Research integrity0.0050.004
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 head0.030
GPT teacher head0.248
Teacher spread0.217 · 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 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

Citations13
Published2014
Admission routes1
Has abstractyes

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Same venueProject Muse (Johns Hopkins University)Same topicCanadian Identity and HistoryFrench-language works237,207