Bibliographic record
Abstract
Abstract It is widely recognized that the innate versus acquired distinction is a false dichotomy. Yet many scientists continue to describe certain traits as “innate” and take this to imply that those traits are not acquired, or “unlearned.” This article asks what cognitive role, if any, the concept of innateness should play in the psychological and behavioural sciences. I consider three arguments for eliminating innateness from scientific discourse. First, the classification of a trait as innate is thought to discourage empirical research into its developmental origin. Second, this concept lumps together a number of different biological properties that ought to be treated as distinct. Third, innateness is associated with the outmoded folk biological theory of essentialism. In response to these objections, I consider two attempts to revise the concept of innateness which aim to make it more suitable for scientific explanation and research. One proposal is that innateness can be defined in terms of the biological property of environmental canalization. On this view, a trait is innate to the extent that it is developmentally buffered against a range of different environments. Another proposal is that innateness serves as an explanatory primitive for cognitive science. This view holds that there exist a sharp boundary between psychological and biological explanations and that to identify a trait as innate means that it falls into the latter explanatory domain. This essay ends with some questions for future research.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.060 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".