Notice bibliographique
Résumé
This paper reports 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), 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. We also reflect on how 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. We hope that Receipts can serve as an example of how to engage the potentials of urban technology while also highlighting some of its pitfalls.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».