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Record W6996998395

The Transmutation of Visceral Desecration: Marginalizing Women, Murder and the Urban Environment Contextualized in Film

2009· article· en· W6996998395 on OpenAlexaboutno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsPraxisExperiential learningDismembermentMovie theaterNarrativeShort FilmGesture
DOInot available

Abstract

fetched live from OpenAlex

This thesis is made up of a 35mm film, reinsertion, and a praxis paper that investigated my film processes and research, concerning the missing and murdered women of Vancouver. The catalyst was my experience living in a city in shock during the Pickton Trial. In 2007 Robert Pickton was charged with 26 counts of First Degree Murder. He was tried and found guilty for six out of a possible 26 murders that took place on his pig farm in Coquitlam. My goal was to create a film that positions the responsibility of the murders on the City of Vancouver, its police, City Hall and residents. My research focused on my personal experiences in the community and feminist discourse. Through the creation of an experimental film about the murders, I explored ideas such as authorship, experiential art and absence as representation. Simultaneously, the film subliminally communicates the trauma of the aforementioned women. This thesis pursues several key questions: Can a city be a sexual predator? Who has the right to tell a story? Can film emote without narrative? It concludes that a society, by inaction, can condone atrocities, that the stories we tell are inherently our own and that trauma can be communicated through subtle movements, gestures or 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)0.002
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 score0.067
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.040
Scholarly communication0.0130.005
Open science0.0010.008
Research integrity0.0020.004
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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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