Syntactic characteristics of Canadian French-language internet memes
Bibliographic record
Abstract
The aim of this research is to identify the main syntactic characteristics of French-language internet memes prevalent in the Canadian digital space. The study examines the sentences used in the textual component of Canadian memes, focusing on their structural composition, illocutionary force, and emotional coloring. The analysis reveals that complex-subordinate and simple declarative sentences are the most common types found in Canadian memes. This indicates a preference among creators for both concise and more detailed contextualization. The syntactic characteristics described in the article reflect a diversity of constructions used to convey sociocultural meanings. They serve to enhance the comic effect of the message and make it more memorable. Through their use, the authors’ desire for emotional and expressive articulation is evident. Therefore, the scientific novelty of this research lies in identifying the preferred syntactic constructions employed by Canadians in French-language memes, thereby expanding our understanding of the cultural and linguistic features of internet communication in Canada and contributing to the development of theoretical and practical knowledge in the fields of digital linguistics and intercultural communication. The results confirm that the unique syntactic features of memes combine elements of humor, conciseness, and expressiveness, allowing Canadians to effectively exchange ideas, emotions, and feelings, as well as to convey cultural specificities in a simple, humorous way.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".