Un regard sur le français inclusif canadien dans une journée de Twitter
Bibliographic record
Abstract
This paper looks at the variation of typographical procedures and agreements related to inclusive French (FI) on the microblogging system Twitter in Canada. The corpus used includes 464 tweets geo-localized in Montreal, Ottawa and Quebec and published on May 12th, 2020. They were sorted into cases of simple inclusion (IS), uniform agreement (AU) and mixed agreement (AM). The results showed that IS (57%) is the most preferred approach. When the tweets presented more than one FI mark, the preferred structure was AM (27%). When compared to the 15% of AU cases, we might conclude that uniform agreement is not common in this corpus. The analysis of AM tokens showed a gradual decrease in agreement marks throughout a given tweet. The preferred FI typographical procedures in this corpus (complete doublets, abbreviated doublets with parentheses, and epicene forms) are consistent with the recommendations of the Office québécois de la langue française, despite the informal context. As my results show a set of alternative overlapping agreement systems, I propose that this heterogeneity is one of the characteristic features of inclusive French.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".