Ethical and Sustainable Engineering Design: A Design for Conviviality Approach
Bibliographic record
Abstract
(i) background: Various philosophers of technology argue against the common misconception that technology is neutral and simply serves human ends. “Reverse adaptation" is the idea that we create technologies to serve us, but, in the end it can often be the human that serves the machine. One key negative effect reverse adaptation is that it thwarts the human goal of sustainability. (ii) purpose: The aim of this paper is to explore an alternative design philosophy, “Designing for Conviviality,” that shows promise in addressing the above concerns. (iii) approach: In this paper a definition of conviviality is given along with its concomitant principles which can serve as a method of evaluating given tools, processes, and practices to aid in determining if they will have positive or negative societal and environmental consequences. (iv) outcomes: This research has resulted in an eight-point list as to the character of convivial tools and systems which enables the assessment of a given technology. (v) conclusions: Designing for Conviviality can serve as a way to guide design processes so that engineers can create products that will have a greater benefit to society and the environment.
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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 | 0.048 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".