The Creation of a French-Language Glossary to Aid in the Instruction of Specialized Undergraduate Courses in Biology
Bibliographic record
Abstract
Teaching specialized undergraduate courses in scientific disciplines such as Biology comes with its own set of challenges, not least of which is associated with the need for students to learn a large amount of highly specific terminology. These challenges are compounded when teaching in a language other than in English, due to a general deficiency in specialized supplementary learning resources, such as textbooks, websites or glossaries. In order to alleviate this burden for my mostly native francophone students, I have compiled an online glossary of specialized biological terms in French, from such sub-disciplines as Ecology, Evolution, Zoology, Botany among others. Furthermore, due to the Greco-Roman origins of most scientific terminology, the glossary has an etymological component that supports my method of instruction in-class and in the online course-related documents, via an “Etymological Approach” to learning biological terminology aimed at increasing word recognition and understanding and to facilitate content learning among the students. I will present how I integrate this online technology and learning approach in (but not limited to) French-language courses at the University of Ottawa, as well as the results of student surveys on their appreciation of these tools and the demonstrated potential for increased learning.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".