Bibliographic record
Abstract
In Chapter 5 we argued that language is stored at the conceptual level, in the form of propositional representations related to the general characteristics of language and independent from the specificity of a given language. Theories of information processing must explain how verbal information is transformed into propositional representations, how one has access to them, how information is stored at the different levels of processing and what the links are between propositional and verbal processes; they must also account for the bilingual's specific behaviour, particularly for the psychological mechanisms which enable him to function alternately in one or the other language while having an extended control on the possible interference. It must equally explain behaviour unique to the bilingual, such as code-switching, code-mixing (discussed in Section 9.3) or the bilingual's capacity to translate. Thus a model of the bilingual's processing must explain at what level of representation the two languages are interconnected and must be informative about the existing relationship between the bilingual's two codes for every mechanism relevant to language processing. In the present chapter we discuss how the bilingual organises, stores and has access to his two languages and propose theoretical frameworks for language representation and processing, which we consider as two separate but interrelated psycholinguistic mechanisms (Section 7.1). We propose a general model of bilingual processing congruent with our approach to language processing (Section 7.2). Finally we discuss briefly the bilingual's non-verbal behaviour and personality (Section 7.3). LANGUAGE STORING AND PROCESSING IN BILINGUALS Psycholinguistic research on bilinguals deals essentially with the relationship between the bilingual's two linguistic codes and several psychological mechanisms involved in language organisation and processing.
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 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".