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Record W4281477701 · doi:10.32920/ifmj.v2i2.1568

Receipts

2022· article· en· W4281477701 on OpenAlexaffvenueabout
Dave Colangelo, Immony Mèn, Patricio Dávila

Bibliographic record

VenueInteractive Film and Media Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsYork UniversityOntario College of Art and DesignToronto Metropolitan University
Fundersnot available
KeywordsEquity (law)Process (computing)Space (punctuation)Internet privacyPublic spaceComputer scienceComputer securityPsychologyPublic relationsSocial psychologySociologyArtificial intelligencePolitical scienceLawEngineering

Abstract

fetched live from OpenAlex

Videos from mobile phones and security cameras are one form of proof of the aggressions experienced by victims and witnesses of various forms of discrimination against equity-seeking individuals and groups in public spaces. At the same time, people do not always feel that they can safely record an incident or easily gain access to archived data. They may also need time and space to process what they have experienced. Furthermore, official channels to lodge complaints are often exclusionary, byzantine, isolating, individuating, demoralizing cul-de-sacs. The result is that the experiences and voices of victims and witnesses of hate and discrimination in public spaces are often diluted or silenced. Computer vision and artificial intelligence tools increasingly deployed as part of “smart city” infrastructures have been proposed as a means to address these issues in real time by predicting, identifying, and aggregating transgressions. Yet, in practice, these tools lack nuance, approximate and automate-out the importance of relationship building with communities, and have generally been used to identify patterns and build predictive surveillance that disproportionately disadvantages already discriminated-against groups. This paper will report on two iterations of our ongoing Receipts project. The project serves as a means to experiment with and propose processes that use social practice and machine learning technologies to prepare testimonies and listeners to more clearly and impactfully speak, hear, and feel what it is like to respond to mimetic trauma and be part of an equity-deserving group in public space. The work is guided by the following question: How can the process of facilitating the preparation and presention of anonymized testimonies of discriminatory aggression in public spaces with the witnesses and victims of said agressions create structures of accountability, solidarity, healing, and community? The first project, Receipts (2020) — https://receipts.publicvisualizationstudio.co/ — was presented as part of The Bentway’s Safe in Public Space program in Toronto, and addressed anti-Asian aggression in public spaces. The second project, Receipts NB, in collaboration with ArtFix, an organization that works with artists with substance abuse and mental health lived experience in North Bay, will address the stigmatization and isolation of this community during the pandemic. It will be presented as part of IceFollies 2023, a week-long public art festival on frozen Lake Nipissing. These explorations emerge from, and reflect upon a theoretical framework that connects visual culture, data creation, visual perception, cognition, machine learning processes, human-computer interaction, social practice and a practice-based framework for research-creation. The work is also informed by an approach to technoscience that uses a critical race, feminist , and decolonial lens. A necessary component of this framework is to prioritize equity through an emphasis on critical pedagogy, co-creation , and participatory art and design practice. The critical media art practices and processes of Receipts do not aim to replace identifying video as an important means of holding people accountable. Instead, we hope that the project can shape technical, social, cultural practices of testimony and listening and make collectivized community resistance resonate more deeply.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.020
GPT teacher head0.320
Teacher spread0.300 · 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

Citations0
Published2022
Admission routes3
Has abstractyes

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