Proceedings 13th Joint ISO-ACL Workshop on Interoperable Semantic Annotation (ISA-13)
Bibliographic record
Abstract
Annotated data is an important resource for the linguistics community, which is why researchers need to be sure that such data are reliable.However, arriving at sufficiently reliable annotations appears to be an issue within the field of discourse, possibly due to the fact that coherence is a mental phenomenon rather than a textual one.In this paper, we discuss recent insights and developments regarding annotation and reliability evaluation that are relevant to the field of discourse.We focus on characteristics of coherence that impact reliability scores and look at how different measures are affected by this.We discuss benefits and disadvantages of these measures, and propose that discourse annotation results be accompanied by a detailed report of the annotation process and data, as well as a careful consideration of the reliability measure that is applied.
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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 | 0.031 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.044 | 0.023 |
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".