TRANSDISCIPLINARY WORKING FOR CULTURE CHANGE IN ETHICAL AGETECH
Bibliographic record
Abstract
Abstract This presentation proposes a new way of understanding ethical culture in AgeTech as a dynamic and changing set of ethical research, design, and developmental processes and practices. By focusing on dynamics of ethical performance and the values, beliefs, and expectations that underpin them, this approach emphasizes ongoing ethical negotiation of all stakeholders involved in the research, design, and development of technology, rather than as a checklist at the research outset. Transdisciplinary working (TW) is proposed as an effective means to implement ethical processes and practices. By involving diverse stakeholders in the development of shared aims and objectives, ethical considerations become detached from the domain of researchers and become a more inclusive stakeholder negotiation. TW acknowledges that the development of new technologies cannot be separated from the people who design and use them; and the social practices, social norms, and social meanings in which they are steeped. The co-creation of socio-technical AgeTech systems is a key strategy for creating culture-change in ethical working environments. By involving diverse stakeholders in dynamic ethical processes across the research pathway, meaningful involvement of all stakeholders can be ensured, resulting in the effectiveness and relevance of AgeTech. Subsequently, technologies that are more responsive to the needs and desires of older people, carers, and professionals, and better reflect the complex ethical landscape in which they operate are created. Overall, this presentation offers a new perspective on ethical culture in AgeTech, one that involves diverse stakeholders in the creation of socio-technical systems that are both effective and ethical.
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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 | 0.047 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.050 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.003 | 0.037 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".