Grammatical gender marking in New Denmark Danish (Canada)
Bibliographic record
Abstract
This article presents a corpus linguistic study of grammatical gender marking in New Denmark Danish (New Brunswick, Canada). The data consist of 2,242 examples of common and neuter gender marking, on (1) the definite suffixes, (2) the indefinite articles, (3) the prenominal definite modifiers, and (4) the possessive pronouns. 39 speakers are represented in the dataset, encompassing 1st-4th immigrant generation speakers. The analysis reveals relatively little deviation from Standard (European) Danish gender marking as only 19 out of the 39 speakers altogether have 47 instances of non-expected gender marking. In spite of the small amount of variation, there are some clear tendencies in the data in comparison with Standard Danish: The definite suffix is extremely stable, neuter nouns in Standard Danish get common gender marking, and ‘complex’ noun phrases with an attributive adjective between the initial gender-marking determiner and the head word show more variation than ‘simple’ NP’s.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".