ARE SOCIOLOGISTS READY FOR ‘ARTIFICIAL SOCIALITY’? CURRENT ISSUES AND FUTURE PROSPECTS FOR STUDYING ARTIFICIAL INTELLIGENCE IN THE SOCIAL SCIENCES
Bibliographic record
Abstract
Current sociology doesn’t have a settled view on what to do with a phenomenon that in the literature has been titled as “artificial intelligence” (AI). Sociological textbooks, handbooks, encyclopedias, and sociology classes’ syllabi typically either don’t have entries about AI at all or talk about it haphazardly with a stress on AI’s social effects and without discerning the underlying logic that moves the prodigy on. This paper is an invitation to a professional conversation about what and how social sciences can/should study “artificial intelligence”. It is based on a discussion of the preliminary results of an on-going three-year research project that has been launched at the ISA Congress in Toronto. The paper examines AI in relation with ‘artificial sociality’. It argues that research on AI-based technologies is flourishing mainly outside established disciplinary boundaries. Thus, social sciences have to look for new theoretical and methodological frameworks to approach AI and ‘artificial sociality’.
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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 | 0.028 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.086 |
| Scholarly communication | 0.019 | 0.041 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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".