Explaining how learners extract ‘formulae’ from L2 input
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it