Digital ethics: Global trends and divergent paths
Bibliographic record
Abstract
This study focuses on how government bodies address digital ethics in their policies. Using a structural topic model (STM), an automated text mining technique, we analyze 71 policy documents published between 2007 and 2022 by national governments and international governmental organizations (IGOs). Our analysis identifies 22 prominent topics clustered into three major themes: the development of responsible technologies , digital rights , and ethical governance . This study provides a comprehensive overview of these topics and themes, including major similarities among and differences between various policies. We reveal the evolution of these themes over time and discuss key elements in policies. While the findings suggest a consensus on core ethical principles guiding the development and uses of digital technologies, significant differences emerge between countries and IGOs regarding the specific topics addressed and the policy priorities. This study contributes to the digital ethics debate by providing a comprehensive overview of prevailing themes in government policies, highlighting both common ground and areas of divergence. We also discuss the implications of these findings and propose directions for future research.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".