The semantics of Afro-Brazilian spirits: Applying Davidson on prior and passing theories
Bibliographic record
Abstract
Abstract Based on fieldwork with two related Afro-Brazilian religions, Umbanda and Quimbanda, this article explores the value of Donald Davidson’s semantic theory for making sense of ethnographic fieldwork. Specifically, we look at the role of scriptedness in communication, including religious ritual. We first clarify the role of social externalities in Davidson’s view of communicative interpretation, which is broader than his initial framework of radical interpretation. We then offer an account of what constitutes communicative and interpretational success, by drawing on Davidson’s account of prior and passing theory. Prior theories are interpreters’ initial hypothetical frameworks, ranging from general (e.g., the rational, intentional nature of self and other, and a shared perceivable world) to local (e.g., assumptions about cultural, social, and institutional contexts). Passing theories are tactical, on-the-fly modifications that we hypothesize in order to get mutual understanding back on track. We introduce the concept of ‘semantic reduction’ to operationalize the view that specific, local social externalities provide clues that help keep interpretation on track. In the case of religious ethnography, these include ritual, doctrinal, narrative, symbolic, material, temporal, and spatial frames that constrain the generation of passing theories. Examples from fieldwork illustrate the potential value of our appeal to Davidson’s ideas.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".