Bibliographic record
Abstract
Introduction As a number of researchers have pointed out, theories of language acquisition must explain both properties of linguistic representations (the form and nature of the grammar) and transition or development (how and why grammars change over time) (Carroll 1996, 2001; Felix 1986; Gregg 1996; Klein and Martohardjono 1999; Schwartz and Sprouse 1994). In other words, there is a need for a property theory as well as a transition theory (Gregg 1996). In previous chapters, discussion has centred on representational issues such as the nature of the interlanguage grammar, the degree to which the L1 grammar determines interlanguage representations, and the extent to which the interlanguage grammar falls within the class of grammars sanctioned by UG. Indeed, most research on L2 acquisition conducted within the generative framework in the last twenty years has focused on issues of representation, in other words, on a property theory of interlanguage. Clearly, however, interlanguage grammars are not static: they change over time. What remains to be considered is how development takes place, in particular, what drives transition from one stage to another. The logical problem of language acquisition (see chapters 1 and 2) motivates a particular kind of representational account, an account that assumes built-in universal principles, in other words, UG. Felix (1986) observed that, in the case of L1 acquisition, far more had been achieved at that time in terms of explaining the logical problem than the developmental problem.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".