Embracing a Material Turn in Software Embedded Design
Bibliographic record
Abstract
Architects are increasingly bundling digital components to¬gether with physical assemblies in their pursuit of responsive (or sentient, adaptive, interactive) architecture where hard¬ware and software work together with physical assemblies to mediate the physical environment in real time. Given that 1) architects are responsible for creating built environments capable of enhancing certain values while downplaying or rejecting others and 2) digital components, such as software and data, have direct spatial, social and cultural agency; this practice, labelled here as software-embedded design (SED), calls for a new set of methods for understanding and support¬ing architects’ engagement with their projects’ computational elements, their soft materials. This paper advances efforts to build this critical computational literacy for SED designers by introducing and testing an analytical framework which offers a new lens through which to consider the digital components used in SED projects. As soft materials become part of an ar¬chitect’s toolkit, it is imperative that the values and objectives embedded in computational components of a project and the critical practice around their use match those projected and exhibited towards its physical elements. The paper is organized in two parts to expand the lenses through which designers engage SED by foregrounding the complex interplay between materials, computational elements and real-world outcomes. The first part of the paper elaborates on how a materialist account of digital technology establishes new obligations by recognizing that managing computational elements’ roles in shaping projects’ (material, spatial, social and ethical) outcomes falls into the domain of the designer. The second part proposes the Project Anatomy analytical framework designed to simultaneously examine a project’s soft and hard materials in order to better grasp the relationship between computational components, physical materials and real-world outcomes. A reflection on the tool foregrounds the unique position of SED designers, through their evolving expertise in both soft and hard materials, to find new purchase on materially-oriented, socially-minded engagements with the computational components increasingly proliferating in our built environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".