Categorical Designations and Methodological Reductionism: Gnosticism as Case Study1
Bibliographic record
Abstract
Abstract Debate continues between reductionists and non-reductionists over sui generis discourse within the academic study of religion. In this article, Gnosticism is explored as a case study for applying methodological reductionism to categorical designations. Metaphysical reductionist approaches to Gnosticism have been present in the field, rendering "Gnosticism" as a transhistorical phenomenon which is irreducible to social scientific methods. After discussing the phenomenological approach of Hans Fonas and the cognitive approach of Ioan Couliano, this article, rejecting both ontological and metaphysical reduction, advocates the application of methodological reductionism. Methodological reduction helps to shift classifications away from conflation with reality to be seen, instead, as analytical devices for theorizing first-order data. A relative approach to the function of classification tools enables us to explore the modes of relation within particular classificatio constructions.
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 | 0.062 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.060 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| 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 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".