Bibliographic record
Abstract
This volume is the outcome of 25 years of research into the neurolinguistic aspects of bilingualism. In addition to reviewing the world literature and providing a state-of-the-art account, including a critical assessment of the bilingual neuroimaging studies, it proposes a set of hypotheses about the representation, organization and processing of two or more languages in one brain. It investigates the impact of the various manners of acquisition and use of each language on the extent of involvement of basic cerebral functional mechanisms. The effects of pathology as a means to understanding the normal functioning of verbal communication processes in the bilingual and multilingual brain are explored and compared with data from neuroimaging studies. In addition to its obvious research benefits, the clinical and social reasons for assessment of bilingual aphasia with a measuring instrument that is linguistically and culturally equivalent in each of a patient’s languages are stressed. The relationship between language and thought in bilinguals is examined in the light of evidence from pathology. The proposed linguistic theory of bilingualism integrates a neurofunctional model (the components of verbal communication and their relationships: implicit linguistic competence, metalinguistic knowledge, pragmatics, and motivation) and a set of hypotheses about language processing (neurofunctional modularity, the activation threshold, the language/cognition distinction, and the direct access hypothesis).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".