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Record W2624773966 · doi:10.26522/gbuujh.v2i0.1498

NATIONALISM, SUFFRAGE & MATERNAL FEMINISM: RACE, GENDER & RELIGION

2017· article· en· W2624773966 on OpenAlexaffvenueabout
Michael Wielink

Bibliographic record

Venuethe general brock university Undergraduate journal of history · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsBrock University
Fundersnot available
KeywordsFeminismNationalismGender studiesSociologyPoliticsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract:The turn of the nineteenth century was an extremely dynamic and formative time in Canadian history as it defined its “imagined community.” Benedict Anderson’s Imagined Communities espoused nation and nationalism through his concept as “imagined communities,” both politically and culturally. “National identities are imagined as both intrinsically limited and sovereign. The nation is imagined as limited because, no matter the size or scope, it is finite in its inclusion and there is always “others” beyond its borders.” Through research on suffragist writings from both men and women, suffragist organizations membership, documents written about the future of Canada, and the concept of maternal feminism there remained a majority of people committed to a future “White Anglo-Saxon Protestant” nation. The women’s suffragist movement and “maternal feminism,” intertwined with nationalism, created a reciprocal effect, which established an “imagined community” that included those who belonged and “others” who clearly did not. Suffragist, armed with new found political influence, endorsed policies of assimilation or legislation to keep Canada from accepting people of “degenerating” races or religions. Clearly, nationalism and an emerging Canadian identity had profound influences on the suffragist movement and maternal feminism, causing once a once marginalized gender to now subjugate “others” based on class, race or religion.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.453
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.036
GPT teacher head0.252
Teacher spread0.216 · 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
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

Citations1
Published2017
Admission routes3
Has abstractyes

Explore more

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