Bibliographic record
Abstract
Variable case marking of pronouns in coordinate noun phrases (CoNPs) is a well-documented phenomenon which has elicited prescriptive censure for centuries. Drawing on the framework of variationist sociolinguistics, this study presents a detailed quantitative analysis of variable case marking in CoNPs in theQuebec English Corpus(Poplack, Walker and Malcolmson 2006), a massive compendium of vernacular speech. Operationalizing a number of extralinguistic and linguistic factors that are claimed to condition variable case marking in CoNPs, multivariate analysis revealed that speaker age and education as well as the syntactic position of the CoNP are key predictors in determining the case of pronouns in these constructions. An important finding is that case marking in CoNPs is highly variable for speakers, suggesting that the Sisyphean efforts of the prescriptive enterprise to impose uniformity on this area of the grammar have been to little avail as far as spontaneous usage is concerned. Comparison of the results with variable case marking in CoNPs in other varieties of English, as well as with diachronic patterns of variability, also raises the possibility that the accusative is increasingly assuming the role of default case in coordinate constructions.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.217 | 0.089 |
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".