Bibliographic record
Abstract
Studies of L2 production have shown that both children and adult learners make use of ‘formulae’, putatively ‘unanalysed’ sequences of words. In this paper I discuss how formulae may arise in L2 acquisition by processes of segmentation. Carroll and MacDonald (Ms. 2009), Carroll et al. (2009) show that even ab initio learners can rapidly segment sound forms from continuous strings. The data are consistent with two approaches to the segmentation of words: words are segmented by tracking co-occurrence statistics over adjacent syllables (transitional probabilities or TPs); the left edges of words are placed just before a strong syllable (a Metrical Segmentation Strategy). In my contribution to this special issue, I address the question of how strings of syllables can be re-analysed as morpho-syntactic categories, their phrasal projections and dependencies. I do this in terms of the Autonomous Induction Theory (Carroll 2001) discussing formulae in particular in terms of correspondences across autonomous and modular representational systems: prosodic, morpho-syntactic, and conceptual.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".