A Performative Response to Sites of Surveillance: The Gorilla Park Project
Bibliographic record
Abstract
The key interlocutor for this project is Gorilla Park, an irregular shaped parcel of land located on a disused railway track, in a Montreal neighbourhood called Marconi-Alexandria, currently a rapidly gentrifying quarter of the city. The uneven development in this part of the city is, in part, due to the increasing presence of Big Data and Smart City start-up companies.The politics of the smart city discourse are deeply entangled with ideas of the ‘right to the city,’ and brings into question: who is the smart city for? What is a smart city? Current trends towards connectivity and mediated urban environments are generally predicated upon a ‘digital agenda,’ wherein the privileged position of smart and intelligent technologies is being furthered. In this exposition one of our aims is to problematize urban sites of surveillance through performative and sonic experiments. Due to the global pandemic, however, we began to collaborate remotely and work with the documentation of our temporary occupation and in-situ exploration of Gorilla Park. As such, this exposition foregrounds an unfolding and iterative approach to artistic research that focuses on performative methods of urban research taking place in contested city sites; foregrounding the experimental use of bespoke sound devices and 360˚ video recordings, to situated methods such as walking, and hacking Google Earth imagery. keywords: acoustic urban ecologies, gentrification, surveillance capitalism, smart cities, performative urbanism, research-creation
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".