Bibliographic record
Abstract
The increasingly anthropomorphic, at times even autocratic, nature of software, exhibited during routine activities such as decision-making, question-answering, or recommending, has only contributed to the enduring issue of software ethics. This paper, after providing an understanding of the unique nature of software and that of ethicality, models ethicality as a meta-quality attribute and proposes an ethically-sensitive, standards-based, technology-and-tool-independent, applicable to software-as-a-product-or-service, semi-formal framework, comprising interrelated conceptual meta-models that provide an understanding of ethicality, user story environment, and user story process. It describes an approach of integrating ethicality naturally and systematically in the user story process, illustrates this approach by means of representative examples from a variety of application domains, and highlights the associated challenges in doing so. It also presents the results of a preliminary survey of students and professionals on their knowledge and experience of ethics in (agile) software projects. Finally, it outlines directions of research, and provides recommendations for those in academia and industry, which have broad implications for ethically-sensitive (agile) requirements engineering education and (agile) software testing.
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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 | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".