Bibliographic record
Abstract
Over four decades ago the so-called Chomskyan revolution appeared to lay the foundation for a promising new partnership between linguistics and psychology. Many have now concluded, however, that the hopes originally expressed for this partnership were not realized. This chapter is about what went wrong and where we might go from here. The discussion first identifies three reasons why initial efforts at partnership may have been inherently flawed — divergent criteria for choosing among competing theories, different ideas about what was to be explained, and different approaches to questions about biology and environment. I then argue that recent developments — especially in associative learning theory, in cognitive neuroscience, and in linguistic theory — may provide a more solid basis for partnership. Next, the chapter describes two possible ways that bridges between the disciplines might develop. One draws on recent psychological research on attention focusing and on linguistic research concerning language constructions. The other draws on the concept of affordances and perspective taking. The chapter concludes that an enduring partnership between linguistics and psychology may indeed now be possible and that there may be a special role for applied linguistics in this new development.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.055 |
| Scholarly communication | 0.014 | 0.030 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".