Searching For Signs of Life in Ontario Universities: An Innovative Method for Evaluating Biodiversity Integration within University Curricula
Bibliographic record
Abstract
This study investigates the degree to which biodiversity concepts are included within university curricula in Ontario and provides a baseline for tracking this. A keyword search of undergraduate and graduate academic calendars from six Ontario universities was conducted. A list of 28 relevant keywords was developed, and university program descriptors were searched for these keywords, while considering core and elective courses within each program. Almost half (49.5%) of the 386 undergraduate programs, and 29.4% of the 327 graduate programs featured biodiversity keywords. Science programs showed the highest degree of integration (74.5% for undergraduate and 37.4% for graduate programs), followed by business programs (57.6% and 38.4%, respectively). The arts and social sciences showed the least biodiversity integration (25.8% of undergraduate and 21.0% of graduate programs). This research method provides a depth of understanding of biodiversity integration within university curricula, although the analysis is limited to the content provided in academic calendars.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".