Bibliographic record
Abstract
This article explores the semantics of spirits and monsters with reference to the Brazilian spirit-incorporation religion of Umbanda (and secondarily to the monster studies literature). Semantics is the study of meaning. The most common, and common-sense, view of meaning roots it in reference, in representation, in signification, in how words match up with things. This article argues that an alternative semantic theory – seeing meaning in interpretation rather than representation – has greater value for making sense of spirits, monsters and gods. The article first characterizes these competing theories of meaning, then discusses problems with the representational assumptions of monster studies, and finally proposes the concept of “semantic reduction” as a tool for interpreting Umbanda’s spirits (and by extension, monsters and gods). This concept notes how attempts to interpret spirits soon run into the expected, the constrained, the pre-established, the scripted. The speech and actions of spirits are semantically reduced because their meanings are constrained and delimited: the semantic networks that constitute these meanings are bound by the religion’s ritual, doctrinal, narrative, institutional and material frames. Making sense of spirits, monsters, and gods is no different than making sense of human beings in “normal” contexts, except for the additional methodological challenge of learning to take account of the former’s unusual contexts.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".