Why this work is in the frame
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Bibliographic record
Abstract
Abstract One of the main difficulties concerning adjective placement is the establishment of clear semantic or syntactic criteria to account for their positioning within the noun phrase (DP) – both with respect to each other and relative to the noun they modify – in a variety of syntactically and morphologically different languages. Hetzron provided a first attempt at drawing general cross‐linguistic observations by looking at several languages of different types. His conclusion was that, based on a certain number of semantic criteria, there is some kind of universal ordering of adjectives, with a few exceptions that are either language‐specific or contextually motivated. As our knowledge of the syntactic representation of DPs improved over the years, analyses of adjective placement became more detailed and complex, and various authors proposed increasingly fine‐grained accounts (e.g. via N‐movement) of the relationship between their position in the DP and their meaning. Recent analyses develop a more detailed and principled account of the mapping between meaning and surface position, as well as a better understanding of the syntactic origin of adjectives. Bouchard addressed a number of problems with the original N‐movement analysis, while at the same time attempting to subsume the issue of adjective placement under more general considerations regarding cognition and language processing. The goal of this chapter is to present a brief overview of adjective ordering analyses in their historical context and present in detail the most current and influential views on the topic. For this reason, the focus will be mainly on Romance (Italian and French) and Germanic (English) languages, the two language types most studied in this respect.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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