Reconsidering inalienable possession with definite determiners in French
Bibliographic record
Abstract
In many Romance and Germanic languages, definite determiners can indicate possession for a subset of nouns that have often been called nouns of ‘inalienable’ possession. This paper addresses the question of why and how the definite determiner contributes to the interpretation of ‘inalienable possession’. Following Freeze (1992) and others, I argue that ‘inalienable possession’ cannot be properly characterized as inalienable and does not involve possession. Relevant ‘inalienably possessed’ nouns are not restricted to body parts, but include a broader set of nouns that are commonly expected to be located in or on the possessor: mental or physical faculties, facial expressions, as well as articles of clothing, protection, and adornment. I argue that the relevant cases are best captured in terms of an analysis that combines a syntactic configuration for locative prepositions (RP in den Dikken’s 2006 sense) with the semantics of weak definites for the ‘inalienable’ use of the definite determiner. All observed restrictions derive from the requirement that the semantic properties of weak definites and the syntactic configuration of the RP need to be compositionally respected. Finally, I propose some ideas about how this analysis can be extended to crosslinguistic variation in German and English.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".