On Embedded Normativity - An Active Inference account of agency beyond flesh
Bibliographic record
Abstract
We introduce and motivate the concept of \emph{embedded normativity} to account for the externalization of social norms in the material environment through human social activity. We ground this notion in the Active Inference framework, and more specifically through the derived Skilled Intentionality framework of ecological perception and action. This framework considers that skilled agent experience the world as a landscape of affordances, or opportunities for action. This landscape is inherently normative, as its experience is tied to the agent's anticipations over its own behaviour (and therefore, indirectly, to its motivations). We emphasize that given this framework, normativity does not exist inside or outside the agent's boundaries, but is brought about by its engagement with the world. We discuss the dynamics of \emph{internalization} and \emph{externalization} by which agents come to project normativity onto elements of their environment, and experience this normativity as a simple attraction toward favoured states. Given this account, we revisit earlier descriptions of the shared material and sociocultural niche enable the broadcasting and integration of norms. Finally, we discuss how embedded normativity can be brought into existence by the perception of humans, and relate our discussion to the ontological stance of participatory realism.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".