Preposition <scp>stranding</scp> and <scp>orphaning</scp>: The case of bare prepositions in French
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In their keynote contribution, Poplack, Zentz & Dion (henceforth PZD; Poplack, Zentz & Dion, 2011, this issue) propose an interesting “scientific test of convergence” (under section heading: “Introduction”) which contains criteria to check whether a particular feature in a given language in contact with another one is due to language contact or not. This is a valiant endeavor with a laudable goal. It is valiant because the answer to this question requires a complex investigation of the languages at issue. It is laudable since it is commonly believed that a given feature of a language in contact with another one is the result of convergence. This belief however is, in general, only a mere conjecture due to superficial similarities of the features at issue, for which no empirical evidence is provided. Yet, there is no doubt that PZD accomplish their endeavor in an outstanding manner. Based on a thorough study of substantial data from Canadian French and Canadian English, they demonstrate in a convincing way how it is possible to reveal whether a given feature is contact-induced or not.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it