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Record W2874397482 · doi:10.1177/1749975518774732

Distribution Matters: Feminist Bookstores as Cultural Interaction Spaces

2018· article· en· W2874397482 on OpenAlexafffund
Kathy Liddle

Bibliographic record

VenueCultural Sociology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsUniversity of Toronto
FundersDirectorate for Social, Behavioral and Economic SciencesUniversity of TorontoEmory UniversityNational Science Foundation
KeywordsSolidaritySociologyInterpretation (philosophy)Cultural spaceVariety (cybernetics)Affect (linguistics)Cultural identitySpace (punctuation)Audience receptionSocial psychologyAestheticsMedia studiesPsychologySocial sciencePolitical scienceLinguisticsCommunicationComputer sciencePoliticsArt

Abstract

fetched live from OpenAlex

To investigate the historical case of North American feminist bookstores, I use archival materials, interviews, and surveys to consider how cultural distribution sites affect the acquisition and interpretation of cultural objects. The findings point to the importance and variety of distributor conditions, including physical space, atmosphere, bookseller characteristics, stock, and audience members. I develop the concept of the cultural interaction space, defined as a location where a distributor, its cultural objects, and its audience converge. These spaces provide opportunities for interaction, observation, and experimentation with both tangible and intangible cultural materials, as well as for identity formation and the development of group solidarity. Future research should consider how variations in cultural distributors and in cultural interaction spaces affect audience reception, interpretation, and use of cultural objects.

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)0.005
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 score0.020
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0170.023
Scholarly communication0.0130.015
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.037
GPT teacher head0.373
Teacher spread0.336 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

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