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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".