Moral Realism, Social Construction, and Realism, Social Construction, and Communal Ontology
Bibliographic record
Abstract
The paper examines two forms of naturalistic moral realism, “Microstructure realism” (MSR) and “Reason realism” (RR). The latter, as we defend it, locates the objectivity of moral facts in socially constructed reality, but the former, as exemplified by David Brink’s model of naturalistic moral realism, secures the objectivity of moral facts in their micro-structure and a nomic supervenience relationship. We find MSR’s parity argument for this account of moral facts implausible; it yields a relationship between moral facts and their natural-scientific constitution that has a queer, slapped-together quality. We argue that the relationship needs to be spelled out by a process of social construction, involving collective intentionality and constitutive rules. We explain how our constructivist model of RR differs from a form of it defended by Michael Smith (1994), which analyzes moral facts by reference not to construction but rather to a hypothetical situation of full rationality. We agree with Smith, as against Bernard Williams, that a rational agent may have reasons for acting that go beyond the agent’s “subjective motivational set,” but we locate such reasons by reference to the agent’s membership in an actual community, and we explore the prospects for moral objectivity given this constraint on moral reasons.
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.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".