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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".