Critical Computational Literacy: A Call for the Development of Socially Aware, Ethically Minded Research within ACADIA
Bibliographic record
Abstract
As computational design matures and strives to move out of the studio/lab and into the real world, multiple dimensions of literacy, valuing the social, the political, and the ethical as well as the technical and the creative, must be acknowledged and supported. This paper evaluates the presence of research advancing socially aware, ethically minded issues currently found in ACADIA’s body of research and offers a strategy for shaping future work in this area. First, data from the CumInCAD index is used to provide a quantitative understanding of the degree to which these issues are represented in ACADIA’s history, with particular focus on the last decade. The paper goes on to articulate key offerings from the field of Software Studies to motivate and identify possible entry points for computational designers to further engage the social and ethical agencies tied to their work. Within this context, the paper argues that the set of lenses used to understand a project's digital components expands to include social, cultural, political, and ethical effects in addition to the technical realities of implementation. The analytical methods presented are intended to support a preliminary survey of ACADIA's literature and serve as a first step in identifying avenues for pursuing socially aware, ethically minded computational design research.
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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 | 0.066 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.067 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".