Bibliographic record
Abstract
Abstract This paper develops a generalization about agreement in German copula constructions described in Coon et al. (2017), and proposes an analysis that ties it to other well-established hierarchy phenomena. Specifically, we show that “assumed-identity” copula constructions in German exibit both person and number hierarchy effects, and that these extend beyond the “non-canonical” or “inverse” agreement patterns described in previous work on copula constructions (e.g., Béjar and Kahnemuyipour 2017 and works cited there). We present experimental evidence to support this generalization, and then develop an account that unifies it with hierarchy phenomena in other languages, with a focus on PCC effects. Specifically, we propose that what German copula constructions have in common with PCC environments is that there are multiple accessible DPs in the domain of a single agreement probe, the lower of which is more featurally specified than the higher (see, e.g., Béjar and Rezac 2003, 2009; Anagnostopoulou 2005; Nevins 2007). We also offer an explanation as to why number effects are present in German copula constructions but notably absent in PCC effects. We then place our account within the broader context of constraints on predication structures.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".