Replication Data for: Zooming in on the semantics of French ingressives: a collostructional analysis
Bibliographic record
Abstract
Dataset abstract: The dataset includes an annotated corpus sample of N = 2000 French sentences with se mettre à or commencer à (1000 observations of each verb). The sample was drawn from the literary corpus Frantext and the journalistic corpus Le Monde (1000 observations from both corpora). The sample is balanced for verb as well as corpus, so we have 500 observations for each Verb-Corpus combination. The data is annotated for 3 variables: Source (corpus), Verb, collexeme. Article abstract: This paper examines the semantic value of the infinitive in the ingressive constructions se mettre à (SMA) and commencer à (COMA) using a distinctive collexeme analysis. We find that the collexemes significant for the construction SMA are fairly homogeneous across the different corpora and can be grouped into the general category of expressive collexemes. The collexemes significant for COMA are more heterogeneous and belong to the category of cognitive collexemes and to semantic fields of sensory and creative acts. The results are compatible with the hypothesis put forward by Verroens and De Cuypere (2023) stating that the overall meaning of the SMA construction is intrinsically punctual. The punctual value of SMA is not only compatible with expressive collexemes, but, moreover, emphasizes their unforeseen and unintentional meaning. Conversely, the incremental value of COMA is consistent with the gradual onset of cognitive and sensory collexemes. Verroens, F., & De Cuypere, L. (2023). French ingressives and (phasal) aspect: A frame-semantic corpus-based analysis. Canadian Journal of Linguistics/Revue Canadienne de Linguistique, 68(3), 435-461. doi:10.1017/cnj.2023.19
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.029 |
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".