Biblical Allusions, Antithetical Structures and Triads: A Stylistics-Rhetoric Appraisal of Some Speeches by Martin Luther King Junior
Bibliographic record
Abstract
In a text, linguistic choices are made by the language user depending on the linguistic resources available to them. The language user relies on appropriate and embellished use of language to make their work interesting and produce a special effect on their audience. For this effect to take place there is an interplay of the linguistic and situational aspects of language. Stylistic analysis therefore aims at explicating how the understanding of a text is achieved, by examining in detail the linguistic organization of a text in relation to the context of situation, and focussing on the affective content. This can be linked to the study of rhetoric which is all about a set of rules and strategies which enable orators to speak well, using language in a more decorative and embellished manner to affect the opinions and feelings of the audience. This is persuasive language, which makes the audience not just to respond emotionally but to identify with the writer’s point of view, to feel what the writer feels. It is against this background that this study is going to carry out a stylistic rhetoric analysis on the language used by Martin Luther King Junior in six of his speeches. In these speeches he uses biblical allusions, antithetical structures and triads emphatically, as stylistic devices that make his speeches more memorable and appeal to the emotions of his audience.
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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".