Bibliographic record
Abstract
JM: I want to ask some questions about the ‘perfection’ of the language faculty. First, a background matter: if you speak of perfection and in particular perfection in design of the language faculty – or at least, the mapping to the SEM interface – you seem to be invited to answer the question, “design for what?” NC: I think that's misleading. That's because of connotations of the word design . Design suggests a designer, and a function of the designed thing or operation. But in biology, ‘design’ just means the way it is. JM: The structure, whatever it is . . . NC: How is the galaxy designed? Because the laws of physics say that that's the way it's designed. It's not for anything, and nobody did it. It's just what happens under certain physical circumstances. I wish there were a better word to use, because it does carry these unfortunate connotations. In a sense – a negative sense – there's a function. If the structure were dysfunctional, it wouldn't survive. And OK, in that sense, it's designed for something. It doesn't mean it's well designed for survival. So take language and communication. Language is poorly designed for communication, but we get by with it, so it's not dysfunctional enough to disappear [or at least, disappear with regard to its use for communication, which isn't its only use, by any means]. Take, for example, trace erasure [or in the more recent terminology of copies, non-pronunciation of copies]. It's good for efficiency of structure, but it's very bad for communication. Anyone who tries to write a parsing program [encounters it] . . . most of the program is about how to find the gaps. Where are the gaps, and what's in them? If you just repeated – if you spelled out [or pronounced or otherwise exhibited] copies – the problem would be gone. But from a computational point of view, that would be poor design, because it's extra computation, so there's no point in it. So you cut it out. And there's case after case like that. So take garden path sentences and islands, for example. Islands prevent you from saying things you would like to say. You can't say, “who did you wonder why visited yesterday.” It's a thought; you know what it means.
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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".