Is There a Need to Address Human Values in Domain Modelling?
Bibliographic record
Abstract
Consideration of human values during software development can improve technology acceptance and minimize its negative societal implications. However, important human values often get ignored during requirements analysis and specification. Modelling is a fundamental requirements engineering (RE) activity. As a pilot study, we consider domain modelling, an RE modelling technique which often forms the basis of system design and hence significantly impacts the whole software development process. This position paper presents initial evidence that human values are indeed reflected in domain models. Consequently, paying inadequate attention to human values during domain modelling may result in negative financial or reputational implications for software organizations. We posit that human values-enriched domain modelling guidelines may help in identifying what stakeholders really want to avoid system rejection and negative social implications and call upon the RE community to integrate human values-enriched guidelines into RE modelling techniques.
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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 | 0.056 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".