Bibliographic record
Abstract
The rhythmic structure in music, referred to as meter, consists of alternating strong and weak beats and higher-level structure.Likewise, in language there is a similar structure: stress is usually an alternation of strong and weak beats, though less regular than in music.In texts that have both linguistic and musical structure, the two rhythms prefer to match but the alignment is not always perfect, though it is systematic: this alignment is regulated by the textsetting grammar.I have investigated one aspect of a textsetting grammar, namely how vowel length differences interact with this system.More specifically, the effects of inherent durations were explored using minimal pairs differing in vowel tenseness, and minimal pairs with word-final /t/ or /d/ were used to probe the effects of voicinginduced allophonic length.This study explores the matching preferences between these linguistic tokens and slots in music which are metrically strong (and also long in duration), and slots which are metrically weak (and also short in duration).These experiments test both metrical structure and duration in music simultaneously and do not distinguish between them.The results show that English speakers prefer to match shorter vowels with short musical notes and longer vowels with long musical notes.The tenseness and allophonic lengthening conditions are both significant, and the participants were more strongly guided by the tenseness of the vowel than the voicing-induced allophonic length.
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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".