Bibliographic record
Abstract
Construction Grammar holds that unpredictable form-meaning combinations are not restricted in size. In particular, there may be phrases that have particular meanings that are not predictable from the words that they contain, but which are nonetheless not purely idiosyncratic. In addressing this observation, some construction grammarians have not only weakened the word/phrase distinction, but also denied the lexicon/grammar distinction. In this paper, we consider the word/phrase and lexicon/grammar distinction in light of Lexical-Functional Grammar and its Lexical Integrity Principle. We show that it is not necessary to remove the word/phrase distinction or the lexicon/grammar distinction to capture constructional effects, although we agree that there are important generalizations involving constructions of all sizes that must be captured at both syntactic and semantic levels. We use LFG’s templates, bundles of grammatical descriptions, to factor out grammatical information in such a way that it can be invoked either by words or by construction-specific phrase structure rules. Phrase structure rules that invoke specific templates are thus the equivalent of phrasal constructions in our approach, but Lexical Integrity and the separation of word and phrase are preserved. Constructional effects are captured by systematically allowing words and phrases to contribute comparable information to LFG’s level of functional structure; this is just a generalization of LFG’s usual assumption that “morphology competes with syntax” (Bresnan, 2001).
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".