Bibliographic record
Abstract
JM: OK, now I'd like to get clear about the current status of Universal Grammar (UG). When you begin to focus in the account of acquisition on the notion of biological development, it seems to throw into the study of language a lot more – or at least different – issues than had been anticipated before. There are not only the questions of the structure of the particular faculty that we happen to have, and whatever kinds of states it can assume, but also the study of how that particular faculty developed . . . NC: How it evolved? Or how it develops in the individual? Genetically, or developmentally? JM: Well, certainly genetically in the sense of how it came about biologically, but also the notion of development in a particular individual, where you have to take into account – as you make very clear in your recent work – the contributions of this third factor that you have been emphasizing. I wonder if that doesn't bring into question the nature of modularity [of language] – it's an issue that used to be discussed with a set of assumptions that amounted to thinking that one could look at a particular part of the brain and ignore the rest of it . NC: I never believed that. Way back about fifty years ago, when we were starting to talk about it, I don't think anyone assumed that that had to be true. Eric Lenneberg was interested in – we were all interested in – whatever is known about localization, which does tell us something about what the faculty is. But if it was distributed all over the brain, so be it . . . JM: It's not so much the matter of localization that is of interest to me, but rather the matter of what you have to take into account in producing an account of development. And that seems to have grown in recent years . NC: Well, the third factor was always in the background. It's just that it was out of reach. And the reason it was out of reach, as I tried to explain in the LSA paper (2005a), was that as long as the concept of Universal Grammar, or linguistic theory, is understood as a format and an evaluation procedure, then you're almost compelled to assume it is highly language-specific and very highly articulated and restricted, or else you can't deal with the acquisition problem. That makes it almost impossible to understand how it could follow any general principles. It's not like a logical contradiction, but the two efforts are tending in opposite directions.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".