Bibliographic record
Abstract
In one popular view, expressed most fully in Borer 2005, word meanings are nothing but unstructured, polysemous ‘blobs’ of content, with no formal properties. It is the syntactic context that shapes their meaning, and only this functional scaffolding delivers the kinds of meanings that the compositional semantics trades in. I call this the ‘Blob Theory’ of root meanings. I am going to argue against the Blob Theory by investigating an overlooked class of nominalizations that show properties unexpected under most classifications (Grimshaw 1990, and following): they exhibit some properties of event nominals (they can be modified by frequent/constant , cf. Borer 2003, Alexiadou 2009) but they nonetheless do not have argument structure. I provide an account of these nominalizations as eventive root nominalizations. I then examine the behaviour of these nominalizations with respect to clausal arguments. I argue that their ability to combine with clausal complements shows that roots have a structured semantics that interacts, as unexpected by Blob Theory, with the compositional semantics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".