Poetic Emotion and Poetic Style: The 100 Poems Most Frequently Included in Anthologies and the Work of Emily Dickinson
Bibliographic record
Abstract
The 100 most frequently anthologized English poems and the work of Emily Dickinson were analyzed empirically in terms of emotion, sound, and style. Results for the 100 poems indicated that poetry was significantly more Pleasant, less Active, more highly Imaged, and in several ways more complex than normal everyday English. In terms of sounds, the 100 poems included significantly more Sad, Passive, Soft, and Pleasant sounds and fewer Active and Nasty sounds than everyday English. Because of relationships between measures, seven factors were sufficient to explain most of the differences among poems. The 100 most frequently anthologized poems are ranked individually in terms of these factors. The poems of Emily Dickinson were significantly less Pleasant, less Active, and more Negative, though containing more Active and Nasty sounds.
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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 | 0.001 |
| 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".