“The Single Thin Ray That Fell upon the Vulture Eye”: Systemic Grammar and Its Use in Edgar A. Poe’s “The Tell-Tale Heart”
Bibliographic record
Abstract
This paper argues that Edgar Allan Poe applies many linguistic techniques in his short story “The Tell-Tale Heart” in order to express the dilemma of a character caught up in the trap of a confused identity, lost subjectivity, and uncontrolled performances. Poe’s story is analyzed in detail to examine the psychology of the performed actions. We analyze some aspects of clause construction, paying attention to ‘who is doing what to whom.’ This analysis is twofold: defining clause construction and discussing why this analysis is relevant and why Poe’s story was chosen for this kind of analysis. In addition, we prove through the grammatical and linguistic choices made by Poe the madness and the instability of the main character in the story. We will be selective in choosing the lines to be discussed, as we focus on the lines that show the main character’s detachment from himself and the rational world he belongs to. The language Poe uses in describing the mad act of killing the old man is highly committed to the psychology and ideology of the text along with its complexities in defining why a man would do what the narrator did.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".