Lap, Twist, Knot. Intentionality in digital-analogue making environments
Bibliographic record
Abstract
This paper discusses a theoretical approach and method of making in computational design and construction. The project examines digital and analogue building practices through a social anthropological and STS lens to better understand the use of technology in complex making environments. We position this with respect to contemporary investigations of materials in architecture which use physical and virtual prototyping and collaborative building. Our investigation extends this work by parsing complex making through ethnographic analysis. In doing so we seek to recalibrate computational design methods which privilege rote execution of digital form. This inquiry challenges ideas of agency and intention as ‘enabled’ by new technologies or materials. Rather, we investigate the troubling (as well as extension) of explicit designer intentions by the tacit intentions of technologies. Our approach is a trans-disciplinary investigation synthesizing architectural making and ethnographic analysis. We draw on humanistic and social science theories which examine activities of human-technology exchange and architectural practices of algorithmic design and fabrication. We investigate experimental design processes through prototyping architectural components and assemblies. These activities are examined by collecting data on human-technology interactions through field notes, journals, sketches, and video recordings. Our goal is to foster (and acknowledge) more complex, socially constructed methods of design and fabrication. This work in progress, using a cement composite fabric, is a preliminary study for a larger project looking at complex making in coordination with public engagement.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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 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".