Bibliographic record
Abstract
I argue that the management of uncertainty by agents in a social world is foundational to the formation of social structures and to the definition of culture. I present a deep Bayesian model for this management of uncertainty in intelligent systems, and I argue for its applicability to cultural sociology. As social systems grow more heterogeneous, management of uncertainty in any participating agent becomes computationally difficult, and I propose that combinations of a small number of layers of reasoning in a deep Bayesian model are sufficient to account for some of the salient ways by which humans manage this uncertainty. Three forces come into play when considering such a model, and each is connected to a particular form of uncertainty. A denotative layer in the model represents uncertainty in the world or environment (ambiguity and risk about outcomes), a connotative layer manages the uncertainty about relationships with other social agents, and the connection between denotative and connotative handles uncertainty about identities of the self and others. Behaviours taken by agent and by others are handled in both layers simultaneously. I show how the tradeoff between these three factors maps to different social structures, and I use use the model to make predictions across a range of domains, and show its relationship to cultural sociological, social psychological, economic and sociological theorizing. I further link this model to Bayesian views of the mind, primarily the active inference model of human intelligence, and compare and contrast to more traditional artificial intelligence.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".