Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it