Bibliographic record
Abstract
We are witnessing a proliferation of design, collaborative technological platforms, websites, and networks dedicated to exchanging information of all kinds. These technologies have a positive role and promote social justice, equity, and the rapprochement of cultures. However, several researchers and civil community members wonder about the use of these technologies, the reasons beyond their emergence, and their designers. While technologies are at the forefront of global development, any system to function well needs a framework to support the experiences that would flow from their environment. In all human progress, some voices urge us to be cautious. Given the preponderance of technologies in our environment, what are the principles to regulate these ecosystems? Many studies have highlighted the moral and ethical issues related to the social use of information technology. There have been previous attempts towards finding ways to create suitable rules for these systems. This paper presumes that many of these conduct codes are more user-oriented, and very few are issued to regulate information technology professionals and designers. Therefore, it is urgent to find a way to design socio systems where several entities (organizations and individuals) can collaborate independently and responsibly on-site in their respective spheres on social projects. In this paper, we are trying to provide different perspectives and lines of thought for responsible and safe use of socio systems and collaborative technology platforms.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.103 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".