Bibliographic record
Abstract
A reader sitting down for the first time to the writings of Origen (ca. 185- 253 CE) might be tempted to accuse him of making arbitrary interpretations of the scriptural text. Though possibly admiring the explanations given, he or she might reproach the ancient commentator for having taken advantage of scripture by setting out his own ideas instead of truly attending to the written word of God.! This has been a common reaction to Origen's interpretive practice. Indeed, the Alexandrian scholar's efforts at biblical exegesis have been viewed as hermeneutical artifacts to be examined and catalogued by modem exegetes but almost always placed in a mental "drawer": curios from a bygone era in the study of sacred scripture. Attempts to recover the strengths of Origen's exegesis and make it speak: to contemporary scholars have garnered few supporters. Consequently, his writings are largely dismissed by members of the biblical guild. I myself have shared this pessimism toward Origen's methodology and the results it can provide for the critical study of the sacred text. Yet, I would like to consider some examples from Origen's scriptural interpretation with the hope of opening a dialogue between the writings of the famed exegete and myself— and, perhaps, others as well.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".