Bibliographic record
Abstract
Abstract The distinction between representations and processes is central to most models of the cognitive science of language. Linguistic theory informs the types of representations assumed, and these representations are what are taken to be the targets of second language acquisition. Epistemologically, this is often taken to be knowledge, or knowledge-that. Techniques such as Grammaticality Judgment tasks are paradigmatic as we seek to gain insight into what a learner’s grammar looks like. Learners behave as if certain phonological, morphological, or syntactic strings (which may or may not be target-like) were well-formed. It is the task of the researcher to understand the nature of the knowledge that governs those well-formedness beliefs. Traditional accounts of processing, on the other hand, look to the real-time use of language, either in production or perception, and invoke discussions of skill or knowledge-how. A range of experimental psycholinguistic techniques have been used to assess these skills: self-paced reading, eye-tracking, ERPs, priming, lexical decision, AXB discrimination, and the like. Such online measures can show us how we “do” language when it comes to activities such as production or comprehension. There has long been a connection between linguistic theory and theories of processing as evidenced by the work of Berwick (The Grammatical Basis of Linguistic Performance). The task of the parser is to assign abstract structure to a phonological, morphological, or syntactic string; structure that does not come directly labeled in the acoustic input. Such processing studies as the Garden Path phenomenon have revealed that grammaticality and processability are distinct constructs. In some models, however, the distinction between grammar and processing is less distinct. Phillips says that “parsing is grammar,” while O’Grady builds an emergentist theory with no grammar, only processing. Bayesian models of acquisition, and indeed of knowledge, assume that the grammars we set up are governed by a principle of entropy, which governs other aspects of human behavior; knowledge and skill are combined. Exemplar models view the processing of the input as a storing of all phonetic detail that is in the environment, not storing abstract categories; the categories emerge via a process of comparing exemplars. Linguistic theory helps us to understand the processing of input to acquire new L2 representations, and the access of those representations in real time.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".