Introductory soil courses: a frontier of soil science education in Canada
Bibliographic record
Abstract
As the focus of soil science education in Canada and elsewhere has shifted towards nonsoil science majors, it is important to understand if and how this has affected the scope of introductory soil science courses. The objectives of this study were to inventory Canadian postsecondary units that offer introductory soil science courses and to document attributes of instructors, students, and teaching approaches in these courses. We surveyed 58% of the instructors of introductory soil science courses across Canada, and most of these courses were offered by geography and environmental science units. The majority of instructors followed a traditional lecture (86%) and laboratory (76%) delivery format, whereas 36% used online teaching resources. Introductory courses were delivered by primarily one instructor, who held a Ph.D. in a tenure track position and in most cases developed the course themselves. Over half of the instructors surveyed used either a required or a recommended textbook, pointing to the need for creation of a Canadian-authored soil science textbook. Several follow-up studies are needed to evaluate teaching methods used in the upper level soil science courses, students’ perceptions of teaching in soil science, and instructors’ knowledge of resources available for online and (or) blended learning.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".