Discourse markers and coherence relations
Bibliographic record
Abstract
We examine how one particular coherence relation, Concession, is marked across languages and modalities, through an extensive analysis of the Concession relation, examining the types of discourse markers used to signal it. The analysis is contrastive from three different angles: markers, languages, and modalities. We compare different markers within the same language (but, although, however, etc.), and two languages (English and Spanish). We aim to provide a contrastive methodology that can be applied to any language, given that it has as a starting point the abstract notion of coherence relations, which we believe are similar across languages. Finally, we compare two modalities: spoken and written language. In the analysis, we find that the contexts in which concessive relations are used are similar across languages, but that there are clear differences in the two modalities or genres. In the spoken genre, the most common function of concession is to correct misunderstandings and contrast situations. In the written genre, on the other hand, concession is most often used to qualify opinions.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".