Bibliographic record
Abstract
JM: Now we switch to human nature . . . NC: OK. JM: Human beings as a species are remarkably uniform, genetically speaking. Yet humans have proven extraordinarily adaptable in various environments, extremely flexible in their ability to solve practical problems, endlessly productive in their linguistic output, and unique in their capacity for inventing scientific explanations. Some, a great many in fact, have taken all this as reason to think that human nature is plastic, perhaps molded by environment – including social environment – and individual invention. The engines of this flexibility and invention are claimed to lie in some recognition of similarities, in induction, or in some other unspecified but general learning and invention technique. This plastic view of human nature has even been thought to be a progressive, socially responsible one. Clearly you disagree. Could you explain why you think that a fixed biologically determined and uniform human nature is compatible with and perhaps even underlies such flexibility, productivity, adaptability, and conceptual inventiveness? NC: First of all, there's a factual question – does a fixed biological capacity underlie these human capacities? I don't know of any alternative. If somebody can tell me what a general learning mechanism is, we can discuss the question. But if you can't tell me what it is, then there's nothing to discuss. So let's wait for a proposal. Hilary Putnam, for example, has argued for years that you can account for cognitive growth, language growth and so on, by general learning mechanisms. Fine, let's see one. Actually, there is some work on this which is not uninteresting. Charles Yang's (2004) work in which he tries to combine a rather sensible and sophisticated general learning mechanism with the principles of Universal Grammar, meaning either the first or the third factor – we don't really know, but something other than experience – and tries to show how by integrating those two concepts you can account for some interesting aspects of language growth and development. I think that's perfectly sensible.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".