Human Subjectivity in Information Practice and <scp>AI</scp> Governance
Bibliographic record
Abstract
ABSTRACT The rise of Artificial Intelligence (AI) introduces a notable tension in the realm of traditional, human‐centric information practices, where human subjectivity has been pivotal in both influencing and being influenced by our interactions with information. An excessive reliance on AI distances humans from practices, potentially diminishing human subjectivity. Additionally, as AI takes on roles once exclusively human, it might constrict opportunities for personal growth and the cultivation of unique insights. Moreover, this technological dependency could dilute the richness of direct human interactions, weakening the fabric of social bonds. These issues—increased AI dependence, AI's encroachment on human roles, and the degradation of social ties—underscore the urgent necessity to revisit our interaction with technology, ensuring it serves to enrich rather than undermine the human experience. In light of this, our panel gathers experts to explore strategies for preserving human subjectivity through cognitive autonomy, creative agency, and social connectivity in the age of AI‐driven information practices. Our dialogue also aims to develop a comprehensive AI governance framework, scrutinized from an interdisciplinary perspective, continually refined in collaboration with academic communities, such as ASIS&T, to solidify and enhance our approaches.
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.
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 | 0.057 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.068 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".