Bibliographic record
Abstract
Abstract Language is a structured form of communication that is unique to humans. Within the first few years of life, typically developing children can understand and produce full sentences in their native language or languages. For centuries, philosophers, psychologists, and linguists have debated how we acquire language with such ease and speed. Central to this debate has been whether the learning process is driven by innate capacities or information in the environment. In the field of psychology, researchers have moved beyond this dichotomy to examine how perceptual and cognitive biases may guide input-driven learning and how these biases may change with experience. There is evidence that this integration permeates the learning and development of all aspects of language—from sounds (phonology), to the meanings of words (lexical-semantics), to the forms of words and the structure of sentences (morphosyntax). For example, in the area of phonology, newborns’ bias to attend to speech over other signals facilitates early learning of the prosodic and phonemic properties of their native language(s). In the area of lexical-semantics, infants’ bias to attend to novelty aids in mapping new words to their referents. In morphosyntax, infants’ sensitivity to vowels, repetition, and phrase edges guides statistical learning. In each of these areas, too, new biases come into play throughout development, as infants gain more knowledge about their native language(s).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.112 | 0.049 |
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".