Perspective Criticism and the Study of Narrative Biblical Literature
Bibliographic record
Abstract
In his recent works, Watching a Biblical Narrative : Point of View in Biblical Exegesis (2007) and Perspective Criticism : Point of View and Evaluative Guidance in Biblical Narrative (2012), Gary Yamasaki has introduced a new methodology, entitled Perspective Criticism, for analyzing biblical literature. The following paper seeks to evaluate whether or not this proposed method is a viable tool for use in the study of biblical texts. In order to do so, the account of the hemorrhaging woman (Mark 5 : 24-34) is used as a test case. In the story, the implied reader is provided with background information about the history and motivation of the hemorrhaging woman. Rather than focusing solely on the protagonist Jesus, the narrator shifts the focus of the story onto the woman and explains her unsuccessful attempts, over the years, to find a cure for her ailment. In employing the Perspective Criticism methodology, the following paper argues that the implied author has purposefully inserted this privileged information, which is achronological to the narrative time of the pericope, in order to elicit empathy from the reader with the woman. The account offers the audience the ability to see previous events from the woman’s point-of-view in order to understand her tragic struggle and emotionally connect with her inner thoughts.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.008 | 0.073 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".