Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article aims to explore the use of cross-impact balances (CIB) to identify scenarios of value change. The possibility of value change has received little attention in the literature on value-sensitive design (VSD). Examples of value change include the emergence of new values and changes in the relative importance of values. Value change could lead to a mismatch between values embedded in technology and the way they are currently considered in society. Such a mismatch could result in a lack of acceptability of technologies, increasing social tensions and injustices. However, methods to study value change in the VSD literature are rare. CIB is a scenario tool that can study systems characterized by feedback loops that are hard to describe mathematically. This is often the case when aiming to define values and their relationships. We demonstrate the use of CIB to identify scenarios of value change using two cases: digital voice assistants and gene drive organisms. Our findings show that CIB is helpful in building scenarios of value change, even in instances where the operationalization of values is complex. CIB also helps us to understand the mechanisms of value change and evaluate when such mechanisms occur. Finally, we find that CIB is particularly useful for social learning and explanatory modelling. CIB can therefore contribute to the design of value-sensitive technologies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it