Bibliographic record
Abstract
Traditional engineering is a process of maximizing utility while minimizing cost to the client. Engineering for sustainability must vastly expand these concepts to become a process of "maximizing social benefit while minimizing negative ecological impact." This paper explores the descriptions surrounding sustainable development, and completes them sufficiently to include units of measure for sustainable technological development. The method to measure how sustainable any technological development proposal would be is derived from these definitions, and from that method the most sustainable alternative can be determined. This paper introduces a relationship between the time it takes for members of a community to meet their needs and the resources consumed by the community. Canadian data are presented to provide an example of this curve. Each community, regardless of scale, will have a unique relationship due to their own assets and aspirations. This approach focuses on the efficiency by which people use their time to meet their needs. It does not address the effectiveness of how people use their time to meet their needs. The proposed method uses this relationship to convert excessive resource consumption into a time cost to the community. A life-cycle analysis (LCA) is undertaken for each alternative design, using human time as the unit of measure. Any alternative that produces a positive net time benefit to the community is sustainable. The alternative that produces the maximum net time benefit to the community is the most sustainable.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".