Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.143 | 0.025 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".