Bibliographic record
Abstract
Although Canada is an officially bilingual country, in which French is stated to be the mother tongue of almost 6.8 million people and English of 17.5 million people (2001 Canadian census), there is not a single general bilingual French-English dictionary on the market of Canadian origin. This means that Canadians are forced to use European-produced bilingual dictionaries of English and French such as the Collins-Robert and the Oxford-Hachette, which, since they are intended principally for the European and U.S. markets, do not include many elements of English and French that are used in Canada. When these elements---these "Canadianisms"---are included in European dictionaries, their treatment is often unsatisfactory as they may be presented in an unsystematic, sometimes incomplete and even confusing manner. The Bilingual Canadian Dictionary Project aims to remedy this situation by providing Canadians and more particularly Canadian writers, editors, translators and interpreters, as well as advanced second-language learners with a linguistic tool specially designed to meet their particular needs. This thesis describes the Bilingual Canadian Dictionary Project and its methodology, as well as the Dictionary itself, its source materials, especially its electronic corpora, and the computer tools used in its compilation. The thesis then discusses the particular features that distinguish Canadian French and Canadian English and summarizes the significant events in the evolution of Canadian unilingual and bilingual lexicography. Finally, examples of regional usage from Canada, North America, France and Britain are presented and their entries in two European dictionaries analyzed and compared with their entries in the Bilingual Canadian Dictionary.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".