“Adverbs and functional heads” twenty years later: cartographic methodology, verb raising and macro/micro-variation
Bibliographic record
Abstract
Abstract Adverbs and Functional Heads: a Cross-Linguistic perspective(Cinque, Guglielmo. 1999.Adverbs and functional heads: A cross-linguistic perspective. New York & Oxford: Oxford University Press)—one of the founding works of “Syntactic Cartography”—combines some of the developments in Syntactic Theory from the 1980s and 1990s with insightful contributions from Linguistic Typology. This paper has two interrelated goals. First, it aims to review the fundamental theses of Cinque’s monography of 1999—which are far from controversial among scholars working in Cartography—; at the same time it provides conceptual support to them. Secondly, it aims to explore some methodological tools of Syntactic Cartography presented and discussed by Cinque, Guglielmo. 1999.Adverbs and functional heads: A cross-linguistic perspective. New York & Oxford: Oxford University Press, namely the so-calledprecedence-and-transitivity tests—after a brief discussion on methodology used to recognise the functional categories, namely the criterion by Jackendoff, Ray. 1972.Semantic interpretation in generative grammar. Cambridge, MA: MIT Press—and the use of the hierarchies as tools to detect intra and interlinguistic variation. With regard to this latter issue, the paper gathers data from Brazilian Portuguese, Canadian English and Colombian Spanish on verb raising. The discussion of the data not only favours Cinque, Guglielmo. 2017. On the status of functional categories (heads and phrases).Language and Linguistics18(4). 521–576 recent updates of his theoretical approach to the cartography of the clause but also shows how Cartography offers a natural scenario for a methodological approach to both micro and macro-variation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".