Bibliographic record
Abstract
Monument Public Address System AR is an interactive augmented reality (AR) documentary revolving around an expanding collection of audio interviews about the past, present, and future of confederate and colonial monuments across the United States. The interviewees include activists, scholars, students, planners, community organizers, and other artists. Some have discussed feelings of exclusion when they see confederate and colonial imagery. Others have evaluated the symbolic violence of the monuments in relation to ongoing racist systems. And others have described potential liberatory sculptural works as replacements. The main goal of the project is to engender thoughtful individual and collective experiences and to support critical and ongoing engagement with public memory and the political, social, and cultural processes responsible for public spaces. As Ana Lucia Araujo, historian and professor at Howard University, writes, “All monuments emerge and disappear because of political battles that take place in the public arena. Likewise, public memory is always political” (Lucia Araujo, 2020). In terms of a participant’s experience of the AR media, once they download and open Monument Public Address System AR on their mobile devices, they will discover 3D virtual objects and animations superimposed on the world around them. When they interact with these objects, short sections of the audio interviews are triggered and play. As they listen to the interviewee’s narratives, participants can explore the virtual animations in relation to the surrounding physical space. It is important to the author-artist that the app is accessible to as many people as possible. While the augmentations are geo-located, and the intention is for participants to circumnavigate confederate and colonial monuments – and the empty spaces where they once stood – while experiencing the AR, the app can be opened anywhere. Moreover, the app is mobile AR, released on both Google Play and the Apple App Store, so that it can be used on a large variety of hand-held devices. It is not dependent on expensive technology. As a cis-gendered middle-class white woman from the southeast of the United States, the author-artist recognizes that her perspective regarding the racist history carried by these monuments is limited. She has initiated the project as a way of discovering, and undoing, her blindspots. The author-artist sets out to support critical thinking about the future of public monuments and spark conversations on the history of slavery and racism in the United States. Monument Public Address System AR is offered as a platform for visual and aural expressions of frustration, anger, sadness, fear, and confusion regarding the racist, unjust and violent narratives that have shaped, and continue to shape, our present and future. It is also built for the enunciation of anti-racist hopes, activities and initiatives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.203 | 0.059 |
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".