Social Kinds, Information and Responsibility
Bibliographic record
Abstract
The theme of this panel is ‘human potential and the information society’. My paper probes two interrelated aspects of contemporary information society, namely the production and circulation of information and ways to categorize people – kinds of people, what I call ‘social kinds’. The recent explosion of information technologies has made the dissemination of such information that much easier. In this paper, I examine the effect of the circulation of information on various groups or kinds of people in which society has a keen interest. I call these kinds ‘social kinds’, the categories used in the human and social sciences to gain knowledge of people and their behavior. Thinking in terms of social kinds and their development helps us understand the impact of a classification can have on people thus classified. My discussion will use an illustration the issue of teen pregnancies and parenting, a pressing social problem in North America and elsewhere,1 for in tance the United Kingdom and Australia. Further, the analysis of social kinds raises, in a fresh way, issues of responsibility in mass media about the production and circulation of information. 1. Social Kinds Following the Canadian philosopher, Ian Hacking, I call the categories used in the social sciences to obtain knowledge about people and their behaviour, ‘social kinds ’ (Hacking 1995).2 These
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".