On the genitive's trail: data and method from a sociolinguistic perspective
Bibliographic record
Abstract
Research on the English genitive (e.g. Rosenbach 2007: 154) reports increasing use of thes-variant. This has been explained as extension to inanimate possessors, a semantic shift (e.g. Hundt 1998; Rosenbach 2002), or due to the pressures of economy in journalism, a register change (Hinrichs & Szmrecsanyi 2007; Szmrecsanyi & Hinrichs 2008). The present work reports on a large-scale sociolinguistic investigation of the genitive in vernacular Canadian English using socially stratified corpora and individuals of all ages. The results show that human, prototypical possessors are 96 per cents-genitive and non-humans are 95 per centof-genitive. Within the small envelope where both forms are possible, we discover that variation patterns quite differently depending on animacy. For humans, use of thes-genitive is stable in apparent time and correlates with whether or not the possessor ends in a sibilant. In contrast, non-human collectives/organizations reveal an increasing use ofs-genitives in apparent time and a favouring effect of short possessors, persistence (when ans-genitive has occurred recently in the previous discourse) and when the individual has a blue-collar job. Groups comprising humans (collectives and organizations), such asour church's youth group, and places that are possible locations for humans (countries, cities, etc.), as inToronto's best restaurant, are the prime conduit for this change. These findings from vernacular speech confirm the extension of thes-genitive in inanimates by semantic extension.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".