The Ideological Inception of Intertextuality and its Dissonance in Current Biblical Studies
Bibliographic record
Abstract
Much confusion surrounds the term ‘intertextuality’, especially regarding its usage in biblical studies today. Though the origin of the technical usage of the term is casually noted by many authors, few seem to note its implications. This essay will retrace the postmodern origins of ‘intertextuality’, namely in Julia Kristeva, and show that its usage in biblical studies today is dissonant to its original intent. In the second part of this essay, I will focus on the work of Richard Hays, who is commonly understood to have first applied the term in biblical studies, in relation to the presence of the Old Testament in the New Testament. After my analysis, I propose an alternative that I consider to be a clearer option, so as not to confuse the current usage of the term with its original intent.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.134 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".