The role of animacy in the nominal possessive constructions of Modern Low Saxon
Bibliographic record
Abstract
The dialects of modern Low Saxon dispose of multiple nominal constructions to express possession. The noun phrases in (1) – (4) exemplify these four different constructions. \n \n(1) sien Huus \n his house \n "his house" \n \n(2) Anna ehr Huus \n Anna her house \n "Anna's house" \n \n(3) Oma's Huus \n grandma=POSS house \n "grandma's house" \n \n(4) dat Huus vun de CDU \n the house of the CDU \n "the house of the Christian Democrats" \n \nAfter establishing these four constructions as a case of syntactic alternation using authentic examples taken from a one million word corpus of Low Saxon texts, I discuss the role of animacy in the domain of nominal possessive constructions. I examine animacy per se and several additional factors that have been connected to animacy in the literature such as concreteness, person, and number. Moreover, I also take a look at the possessive relation between possessor and possessum and discuss how it is connected to animacy. \n \nMy research shows that animacy plays a very important role for the choice of possessive construction in Low Saxon and that the animacy level of the possessor is much more important than the animacy level of the possessum. The three prenominal constructions (1) – (3) in which the possessor phrase precedes the possessum phrase are generally used with more animate possessors than the postnominal construction in (4). This is in line with the few comments on these constructions given in descriptive grammars on Low Saxon, e.g. Saltveit (1983), and with results of previous studies on the possessive alternation in English (eg. Leech et al. 1994 and Rosenbach 2002). The fact that the prenominal possessive constructions in (1) – (3) pattern more closely together than each does with the postnominal possessive construction in (4) lends further support to Rosenbach's theory for English that the influence of animacy on the choice of possessive construction is based on the different linear order of possessor and possessum. However, I will also show that there are some differences between the prenominal constructions regarding the role of animacy.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".