The Linguistic Analysis of Indictments in English Through Speech Acts and Evaluation Frameworks
Bibliographic record
Abstract
This study aims to analyze the linguistic features of indictments in English using speech act theory and appraisal frameworks. The theoretical background draws on Searle's (1969) taxonomy of speech acts and Martin and White's (2005) appraisal model for analyzing interpersonal meaning. The methodology employs qualitative textual analysis to code speech acts and appraisal resources in a dataset of 10 English indictments sourced from legal databases. Preliminary findings identified assertive speech acts describing alleged facts, directive acts asserting charges, and expressive and declarative acts conveying the prosecutor's stance. The analysis also revealed linguistic strategies for construing attitude and graduating intensity. Key results demonstrate how prosecutors rhetorically utilize speech acts and evaluation to formally assert charges, commit to proving accusations, and align readers against defendants. This research enriches our understanding of indictments from applied linguistic and discourse analytic perspectives. It provides practitioners with insights into crafting more deliberate indictments through language choices. Further research can expand the framework cross-culturally and to other legal genres.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".