Bibliographic record
Abstract
This article discusses the computational and material interplays embedded in the making of [re]capture, a research–creation project combining a bio-inspired installation that materialises particulate matter, together with outdoor sensing instruments that collect atmospheric data in at-risk neighbourhoods (Montreal, Canada). With impacts on health and the environment, habitual and slow forms of exposure to atmospheric pollution (Hsu 2016) outline the relationality of air and the porosity of bodies, both human and more-than-human (Nieuwenhuis 2016; Albano 2022). What kind of technical objects, and material-esthetics can “negotiate a rapprochement” (Gissen 2009, 22) with the invisible materiality of air? At the intersection of critical and bio-design, mechanical engineering, and computer science, [re]capture delves into this question through the lens of ‘filtration,’ simultaneously envisioned as a physical process for attending to atmospheric pollution, and as a generative concept for interpolating technology, materiality, and the city. While the artwork iterates a virtual testing model (Blender and ossia score) with physical prototyping, the article examines how to compose with air through digital simulation and scoring to create new alliances between porous meshes, bioindicators, data, particulate matter, light, wind, and electronics. It also asks How to design installations that embody and materialise the affective properties of air? Attending the speculative trajectory of this process, the article draws on feedback from computer-aided simulation techniques and collaborative experiments in residency spaces to investigate the ‘scoring’ of [im]materiality and explore the spatio-temporality of air.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".