Bibliographic record
Abstract
Abstract Words are the backbone of language activity. An average 20-year-old native speaker of English will have a vocabulary of about 42,000 words. These words are connected with one another within the larger network of lexical knowledge that is termed the mental lexicon. The metaphor of a mental lexicon has played a central role in the development of theories of language and mind and has provided an intellectual meeting ground for psychologists, neurolinguists, and psycholinguists. Research on the mental lexicon has shown that lexical knowledge is not static. New words are acquired throughout the life span, creating very large increases in the richness of connectivity within the lexical system and changing the system as a whole. Because most people in the world speak more than one language, the default mental lexicon may be a multilingual one. Such a mental lexicon differs substantially from a lexicon of an individual language and would lead to the creation of new integrated lexical systems due to the pressure on the system to organize and access lexical knowledge in a homogenous manner. The mental lexicon contains both word knowledge and morphological knowledge. There is also evidence that it contains multiword strings such as idioms and lexical bundles. This speaks in support of a nonrestrictive “big tent” view of units of representation within the mental lexicon. Changes in research on lexical representations in language processing have emphasized lexical action and the role of learning. Although the metaphor of words as distinct representations within a lexical store has served to advance knowledge, it is more likely that words are best seen as networks of activity that are formed and affected by experience and learning throughout the life span.
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 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".