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Record W4313596036 · doi:10.1007/978-3-031-14171-3_8

Vidéo de Femmes Dans le Parc: Feminist Rhythms and Festival Times Under Covid

2023· book-chapter· en· W4313596036 on OpenAlexaffabout
Ylenia Olibet, Alanna Thain

Bibliographic record

VenueFraming film festivals. · 2023
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsMcGill UniversityConcordia University
FundersSchool of Oriental and African Studies, University of LondonUniversiteit Utrecht
KeywordsExhibitionMandateCoronavirus disease 2019 (COVID-19)QueerMovie theaterSociologyEmbodied cognitionMedia studiesVisual artsGender studiesArtPolitical science

Abstract

fetched live from OpenAlex

Abstract Vidéo de Femmes dans le Parc (VFP) (Women’s Videos in the Park) is a summertime open-air screening of independent short videos, held annually since 1991 at Park La Fontaine in Montreal, Canada, by Groupe Intervention Vidéo (GIV), an independent feminist/queer distribution company. In this essay, we explore VFP’s historical use of public space and its reimagination under Covid’s urgent sanitary crisis and chronic social inequities. Within the media ecology of Montreal’s outdoor cinemas, we see GIV’s creative decision to move VFP online during Covid as part of a longer history of alternative media’s unconventional exhibition modes that address social inequalities. As such, we first situate VFP within GIV’s wider mandate of dissemination of video work by women. We then analyze VFP’s “visual architecture” under Covid, stressing the organizers’ original strategies to reproduce a sense of eventness even through online exhibition. We conclude with questions of embodied and affective labor, including audiences’ wellbeing, artist renumeration, and self-care, that the shift online entails for the organizers of VFP.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.275
Threshold uncertainty score0.547

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.007
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.052
GPT teacher head0.248
Teacher spread0.195 · 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
GenreOther

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

Citations2
Published2023
Admission routes2
Has abstractyes

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