The transformation of the quebeckers'internet discourse (on the material of ConneriesQc.com account on facebook)
Bibliographic record
Abstract
Nowadays the problem of language purity is one of the most actively discussed issues in Quebec. The language policy carried out in the course of several decades by the government of this Canadian province resulted in the transition from the dominant usage of English in all the spheres to the priority-driven usage of French in both interpersonal and institutional communication. Though, after thorough analysis of the content of ConneriesQc.com (a famous Quebec French humoristic site) account on Facebook (5-10 March 2016) [URL: https://www.facebook.com/conneriesqc/] we came to the conclusion that English has a considerable influence on the Quebeckers' Internet discourse that is the reflection of the current language situation in Quebec. We divide samples (collected using continuous sampling) of Internet discourse transformation into several categories: transformation of discourse at morphological, lexical and syntactic levels. Our research has also discovered the frequency of code mixing in the Quebeckers' Internet discourse; the tendency of words spelling simplification in English manner. Basing on the data obtained, we made a conclusion that the possible reasons of the transformation of the Quebeckers' Internet discourse are greater social prestige of English in Canada and its global expansion and also the long period of the annihilation of the French language by the Canadian government.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".