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Record W7084396210 · doi:10.18716/ojs/gefo/2017.2398

Review: Kristen Hogan: The Feminist Bookstore Movement: Lesbian Antiracism and Feminist Accountability

2024· article· en· W7084396210 on OpenAlexaboutno aff

Bibliographic record

VenueUniversitäts- und Stadtbibliothek Köln · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsHoganLesbianNarrativeFeminismFellFeminist philosophy

Abstract

fetched live from OpenAlex

In lieu of an abstract, here is the first paragraph of the review: If Kristen Hogan’s time at the Toronto Women’s Bookstore was “a love story,” (Hogan 86) then her book The Feminist Bookstore Movement: Lesbian Antiracism and Feminist Accountability is the longest ‘Dear John’ letter ever written. While Hogan fell in love with Feminist Bookstores during the 1990s and the 2000s, this books chronicles the long war and eventual defeat of feminist bookstores by capitalist bookstores. Hogan charted the rise and fall of the feminist bookstore movement through the lifespan of the Feminist Bookstore News, beginning in 1976 and ending at the dawn of the 21st century. Through creating the historical narrative of the Feminist Bookstore movement, Kristen Hogan also introduces the ideas of lesbian antiracist accountability, the feminist shelf, and feminist remembering. These three concepts are important additions to feminist thought and should be further analyzed within the discipline.

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)0.003
metaresearch head score (Gemma)0.012
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: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.007
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0100.003

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 head0.021
GPT teacher head0.250
Teacher spread0.229 · 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
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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