Where There Is Dirt, Is There System? Revisiting Biblical Purity Constructions
Bibliographic record
Abstract
This article contends that biblical scholarship on impurity has often been concerned with attempting to find one symbolic system underlying Israelite purity constructions. This tendency is clear in the work of Mary Douglas and Jacob Milgrom, but even in more recent scholarship the tendency to treat the diverse body of texts discussing impurity as a ‘system’ has continued. Even recent attempts to place all of these texts into two or more categories of impurity have had to force biblical texts to fit categories that supposedly encompass all of the Hebrew Bible. This article presents various important inconsistencies among the purity constructions of different biblical texts in order to demonstrate that these constructions are not in fact ‘systematic’. There is no ‘system of Israelite impurity’. Moreover, in positing such a system, scholars have displayed assumptions and utilized methods that are at odds with those of contemporary ritual studies. This article argues instead for an embodied approach to studying Israelite purity constructions that moves beyond Cartesian dichotomies and seeks to contextualize the evidence from different biblical texts, treating differences between texts not as obstacles but as analytical opportunities.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.068 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".