Narrative Appraisal as a Linguistic Approach to Evaluation in Text: The Case of Pronouns
Bibliographic record
Abstract
Unlike modern narrative, which often goes into great detail in order to develop characters and themes, the narrator in the Old Testament is reticent, and the narrative is typically terse. There are many ambiguous passages involving actions of dubious propriety, resulting in readers being uncertain how to assess characters and draw ideological conclusions from their actions. It is too easy for modern readers to filter interpretive decisions through their presuppositions and values. Appraisal theory, an area of systemic functional linguistics, acts not to eliminate but to constrain the subjectivity of the interpreter and increase the transparency of the process by looking for specific linguistic signals in the text that can be presented as evidence. These instantiations are drawn mainly from the interpersonal metafunction, but also involve the textual and ideational metafunctions. J.R. Martin and P.R.R. White developed a system network through which text is processed in order to identify evaluative language; however, their work is based primarily on contemporary English texts of a rhetorical nature, such as political speeches and reviews. This article presents a modified system network, the “Narrative Appraisal Method,” adapted for Hebrew narrative texts. It operates not only at the level of the clause but also at higher levels of discourse. It takes into consideration the characteristics of narrative and the point of view of the evaluator. The article provides specific examples of the results the methodology yields from the book of Judges, focusing on situations in which pronominal forms play a relevant role.
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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 | 0.024 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".