Metrical complexity in Russian iambic verse: A study of form and meaning (2002)
Bibliographic record
Abstract
Readers of poetry make aesthetic judgements about verse. It is quite common to hear intuitive statements about poets' rhythms, such as 'this poet sounds complex'. Yet, it is far from clear what these statements really mean. In the traditional theory of Generative Metrics (Halle and Keyser 1971, Kiparsky 1975, 1977, Hayes 1989) the complexity of poetic meter was understood as a deviation from the monotonous metrical template. This dissertation proposes a new way of measuring verse complexity. I argue that complexity is the ability of a poet to control a number of independent linguistic and non-linguistic domains at once. The dissertation includes three case studies. In chapter 2 I show that 18th and 19th century Russian iambic tetrameter is a case where poets deviate from meter, and at the same time, control the overall statistical distribution pattern and certain proportions of the deviating lines. In chapter 3, I show that certain deviating patterns in Brodsky's iambic verse written in Russian consistently correlate with the theme of exile. Thus, Brodsky simultaneously controls rhythm, semantics and the general statistical distribution pattern. Chapter 4 shows that Brodsky creates a metrical elision rule, which involves a simultaneous manipulation of metrics, phonetics, and phonology. This dissertation contributes to the linguistic study of poetic meter by proposing a unified cognitive explanation of various aesthetic judgements about verse.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".