The trajectory of φ-features on Old French D and n
Bibliographic record
Abstract
Abstract Old French (OF) determiners (D), which are optional, show a three-way split between definite (def), indefinite(indf), and expletive(expl)D. We develop a nano-syntactic analysis of these three paradigms, according to which the nominal spine is associated with a series of functional heads that include Number, Gender, D, and Kase. We test the predictions of the formal analysis with a quantitative analysis of corpus data from two 12thcentury Anglo-Norman texts –Le voyage de saint Brendan(B) andLais de Marie de France(MdF) – which indicates that over a 60-year span, there are changes in the distribution of D. This presents itself in three ways. First, a decline in expletive D inMdFcorrelates with an increase in the use of D with masculine (m) non-count nouns (nNON-CT) Second, whileBlacks an overt indefinite plural (pl) D,MdFhas one in the form ofdes. Third, with count nouns(nCT), while feminine (f) nouns favour the absence of determiners inB, there is no gender effect inMdF. While the first two changes are predicted by the formal analysis, the third is not. More broadly, the results of our quantitative study provide a more nuanced picture of the factors that govern the distribution of D in OF: they confirm that – relative to conditioning the absence of D (D-drop) – definiteness, grammatical function, and number are stable factors, gender is not a stable factor, and word order does not play a significant role.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".