Creating a New “Great Divide”: The Exoticization of Ancient Culture in Some Recent Applications of Orality Studies to the Bible
Bibliographic record
Abstract
One of the main contributions of orality studies in Old Testament/Hebrew Bible studies has been to reject the thesis of the “great divide,” which posited a gulf between oral and written cultures of the ancient world. While critique of the thesis is to be welcomed, some of the criticisms have set up an artificial great divide of their own. This new divide exoticizes ancient culture by exaggerating the differences between modern and ancient cultures. I caution against this trend and show that this exoticizing of ancient culture can be seen in the perceived function of ancient and modern texts and the perceived differences between the mind-set of ancient literates and modern literates. I suggest that a balanced approach needs to take into account the complexity of both orality and literacy in reconstructing the function of scribes and their texts in ancient Israelite circles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".