Bibliographic record
Abstract
Cette étude a pour but d’expliquer l’origine de ma + Inf, indice du futur à la 1ère pers. du sing, en québécois et dans les créoles à base française : en présence d’attestations correspondantes dans des dialectes français, il est presque certain que ma a été « exporté » dans les anciennes colonies françaises et qu’il ne constitue pas une innovation du français d’outre-mer. Certaines particularités de l’emploi de ma (utilisation à la 1ère pers. du sing, et rareté de cooccurrence avec la négation) se retrouvent dans toutes les variétés du français en question ici : langue standard, dialectes, variétés régionales d’outre-mer et créoles. La genèse de ma est expliquée par la grande fréquence de la 1ère pers. du sing, dans des contextes futurs et le manque d’accent qui repose sur je m’en va(i)s, signe proclitique du futur.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".