Topical Segmentation: a Study of Human Performance and a New Measure of Quality.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a large-scale study of how people find topical shifts in written text, 27 annotators were asked to mark topically continuous segments in 20 chapters of a novel. We analyze the resulting corpus for inter-annotator agreement and examine disagreement patterns. The results suggest that, while the overall agreement is relatively low, the annotators show high agreement on a subset of topical breaks – places where most prominent topic shifts occur. We recommend taking into account the prominence of topical shifts when evaluating topical segmentation, effectively penalizing more severely the errors on more important breaks. We propose to account for this in a simple modification of the windowDiff metric. We discuss the experimental results of evaluating several topical segmenters with and without considering the importance of the individual breaks, and emphasize the more insightful nature of the latter analysis. 1
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it