Grammar Competition Explored in Two Case Studies: The Null Subject Stage in English-Speaking Children and the Variation Observed in Old English
Bibliographic record
Abstract
Grammar competition theory postulates that variation in a speaker is the result of different grammars competing against each other. This study performs an analysis of two case studies of empirical observations attributed to possible grammar competition — subject drop in English-speaking children and variation observed in Old English. Children in an English-speaking environment drop subjects early on during acquisition. Orfitelli and Hyams (2012) find that young English-speaking children mistakenly interpret imperative null subject utterances as declaratives. They suggest that this misinterpretation can be attributed to performance factors, which leads to grammar competition and subsequently subject drop in English children. We introduce a novel computational parameter to quantify this developmental misinterpretation and fold the parameter into two sets of simulations to model the acquisition of English grammar. The results from the simulations lend support to Orfitelli and Hyams' proposal. More generally, this work presents a framework for future computational modeling of grammatical acquisition in conjunction with other critical factors that shape a child's course of language acquisition. Corpus linguistics has two main research approaches: “corpus-based” and “corpus-driven”. Corpus-based studies involve an exploration of the corpus guided by predefined hypotheses or intuition. On the contrary, a corpus-driven study makes minimal assumptions and forms an analysis after an unbiased exploration of the corpus, uncovering patterns not considered under the microscopic focus of a hypotheses-driven search. The second part of the thesis revisits the observed variation in Old English. This variation has been attributed to competing grammars in various corpus-based studies. This thesis presents the first corpus-driven exploration of the York-Toronto-Helsinki Parsed Corpus of Old English Prose, with the aim of investigating a grammar competition account. Using a corpus-driven search method and adopting a broader set of assumptions that drives the analysis that ensues, we are able to document and re-analyze an exhaustive list of word-order patterns, subsequently enriching/challenging previous findings. The contributions in this work add substantially to our understanding of language variation and grammar competition in two distinct and disparate use cases. More importantly, this work borrows from work done in the computational field and constructs robust methodologies that could guide future research in this highly interdisciplinary field of research.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".