Bibliographic record
Abstract
Background and Objectives Bilingualism in Canada is enacted in the Charter of Rights and Freedoms. However, there are numerous barriers to its implementation in the workplace, including the dominance of English over French and a lack of promotion of Francophone culture. The rapid development and recent availability of AI-based language meeting support and other technologies may become a game changer in the practice of bilingualism in Canada in coming years. Methods/Description Our team mostly collaborates in a virtual environment. Its members are largely a mix of unilingual Anglophone and bilingual Francophone individuals, with varying degrees of fluency and comfort in English and in French. Recently, our team started to experiment with the use available technologies in the virtual collaboration space. Furthermore, the team has recently started using bilingual written communication by leveraging automatic translation tools. During our presentation, we will demonstrate how such a meeting can be run using innovative technologies. Results/Product(s) Meetings previously conducted in English, or requiring bilingual team members to translate and summarize for unilingual team members can now be conducted more seamlessly. Members can now express themselves in the language of their choice, and follow the conversation by reading the live translated captions. During presentations, slides can also be automatically translated. Conclusions/Implications/Impacts/Next Steps The latest technologies may become a game changer in the Canadian bilingual landscape, by easily enabling effective communication in both French and English, verbally as well as in writing. These new opportunities also raise several concerns. Firstly, about which organizations develop these technologies, and how misuse or diversion may occur. Secondly, there are confidentiality, privacy, and security challenges linked to data processing of written messages or voice recordings outside of the organization. Recent AI-based machine translation enables team members to express themselves and understand what is said in the official language of their choice, and ensure they are understood by other team members. This is particularly important to unilingual individuals, who may feel excluded or disadvantaged in a bilingual workspace. This presentation aligns with the Official Languages Health Program goal of ‘expanding access to French-language health training programs’, including conducting health research in French. By sharing our lived experience with a pan-Canadian research project, we will introduce participants to hands-on, innovative tools to support conducting unilingual-inclusive health research in both official languages.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.015 |
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".