Sustainable Society: Wellbeing and Technology—3 Case Studies in Decision Making
Bibliographic record
Abstract
Throughout history, technology has provided many and significant improvements to the way we live, but the current pace of development now often exceeds the ability for the full potential of any technological innovation to be explored and implemented before further innovations are introduced. This pace of change results both in missed opportunities for a technology in its ability to contribute to effective solutions in addressing issues such as reducing adverse environmental impact or improving the health of society. In considering the nature of technological innovation and development, the associated engineering design processes can themselves be characterized as being associated with a highly complex, iterative problem-solving exercises, involving the integration and synthesis of a wide range of technologies. This in turn requires the design team to manage trade-offs across a range of primary constraints, as for instance embodied energy in manufacturing, energy consumption in use, capital costs and operating and resource recovery costs. Further investigation into the complexity of societal issues and means for achieving a more effective and fuller utilization of both existing resources and technologies is necessary to place sustainability as a priority of the decision making process. To support discussion and provide context, three case studies are presented. The first case study examines a strategic framework adopting metrics aligned with environmental issues used as proxies for evaluating wellbeing and common good. The second case study examines the specific contribution of eHealth to wellbeing and the balance of technological, societal and political issues in determining outcomes. The third case study considers how technology might be embedded as part of the process of obtaining meta-data from within a small rural community to demonstrate the impact of mitigation strategies associated with the reduction of its carbon footprint, and hence on climate change. In doing so, the paper seeks to bring together issues surrounding environmental problems in relation to a technology driven engineering design process while positioning them in the context of social benefits arising from sustainable decision making.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".