In Search of L1 Evidence for Diachronic Reanalysis: Mapping Modal Verbs
Bibliographic record
Abstract
The lexical mapping of abstract functional words like modal verbs is an open problem in acquisition (e.g., Gleitman et al. 2005 Gleitman, Lila R., Cassidy, Kimberly, Papafragou, Anna, Nappa, Rebecca and Trueswell, John C. 2005. Hard words. Journal of Language Learning and Development, 1(1): 23–64. [Taylor & Francis Online] , [Google Scholar]). In diachronic linguistics it has been proposed that learner mapping errors are responsible for innovations in the historical record (see Kiparsky 1974 Kiparsky, Paul. 1974. “Remarks on analogical change”. In Proceedings of the First International Congress of Historical Linguistics, Edited by: Jones, Charles and Anderson, John M. 257–276. Amsterdam: North Holland. [Google Scholar]; Roberts & Roussou 2003 Roberts, Ian and Roussou, Anna. 2003. Syntactic change, Cambridge: Cambridge University Press. [Crossref] , [Google Scholar], among others). This suggests that child error patterns should be consistent with historical changes. I studied the acquisition of modal lexemes by flavor (e.g., ability, epistemic) in order to assess the validity of this proposal in relation to the mapping problem. A preference task and a sentence-repair task were designed to address the question: Do children make structural mapping errors that, if left unchecked, are compatible with the innovations we see in the historical record (e.g., deontic > epistemic)? This study provides experimental data on the acquisition of modal lexemes by flavor and some long-awaited preliminary support for the hypothesis that child learners drive historical change.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".