Comparison of Inter-rater Reliability of Human and Computer Prosodic Annotation Using Brazil’s Prosody Model
Bibliographic record
Abstract
The current study examined whether the computer annotations of prodody based on Brazil’s (1997) framework were comparable with human annotations. A series of statistical tests were performed for each prosodic feature: tone unit (two accuracy scores and Pearson’s correlation), prominent syllable (accuracy, F-measure, and Cohen’s kappa), tone choice (accuracy and Fleiss' kappa), and relative pitch (accuracy, Fleiss' kappa, and Pearson’s correlation). We considered one population to be the inter-rater reliability scores between the three human coders and the other population to be the inter-rater reliability scores between the computer and the three humans. If the differences between these two populations were significant, then the computer and human annotations were considered not comparable, but if the differences were not significant, then the computer and human annotations were considered comparable. The results indicated that the computer and human annotations were comparable for tone choice and not comparable for prominent syllable. For tone unit, two of the t-tests provided evidence that they were comparable and one did not. The relative pitch t-tests showed a significant disparity between the estimates of relative pitch by the humans and the computer’s actual relative pitch calculation.
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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 | 0.048 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".