Cultural healing through computational crisis: disruption as a catalyst for alternate technologies
Bibliographic record
Abstract
Our presentation will take the form of an interactive dialogue centred around the ramifications of electronic devices: tackled both from our Canadian (Cyrus Khalatbari) and Ghanaian (Akwasi Afrane) arts and design perspectives. Developed moreover as a conversational artist talk nuancing and questioning our mainstream ubiquitous narratives around digital (im)materiality, we will develop this contribution around our two intersecting research paths and inquiries that 1) shed light on the planetary toxicity and energy-crisis of the internet and 2) combine Afro-futurism with cultural healing through disruption. Our presentation will be centred around three sections; sections where we will expand on theoretical, methodological and design insights and rhizomes about our own practices and research. The first section will gravitate around the culturally-situated concept of “new” media; as the core model and injunction that underlies the lifecycle of electronic devices. Here, we will challenge the linear history of technology in order to recontextualise our current technologies as an assemblage of networks and infrastructures where both old and new intertwine. Our second section will expand from the use of science fiction inside our design practices : serving here as a catalyst to question, re-appropriate, disrupt and subvert our western “electronic-waste”(e-waste) narratives and discourses. Our third and final section will expand on two projects and design case studies that disrupt and challenge our normative views of technology: “iPhone/earth” (Khalatbari, 2023) and “TRONS” (Afrane, 2022). Drawing moreover from our previous sections, we will untangle how these projects, through their situated disruptions and re-appropriations of e-waste, draw from science-fiction in order to critically address through design the energy crisis and utopian/Afro-futuristic characteristics and potentials of alternative technology making and electronic waste.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.046 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".