Identifying learning outcomes for a Canadian pedology field school: addressing the gap between new graduates’ skills and the needs of the current job market
Bibliographic record
Abstract
To address concerns among members of the Canadian Society of Soil Science (CSSS) regarding the discipline’s capacity to train new soil professionals, specifically in pedology and field skills, members of the CSSS’s soil education and pedology committees have proposed to develop a pedology field school. To aid in the selection of learning outcomes that are relevant to professional practice, an online survey was sent to Canadian soil professionals within private industry and governmental organizations. Professional feedback was also requested regarding the creation of a web-based national soil education resource and the certification of soil pedological skills. According to the survey results, the quality of new graduates’ pedology and field skills was perceived as poor. Certain soil field skills and knowledge were thought to be either completely absent from the current Canadian curriculum (e.g., spatial variability of soil processes), or not well mastered by graduates (e.g., interpreting soil survey reports). Important learning outcomes were identified, such as interpreting soil survey information, soil mapping, and soil-landscape classification with soil description–classification and soil genesis content needed as a refresher. Taking into consideration existing regional field schools, we recommend that the CSSS co-create, where needed, and coordinate, where they already exist, regional pedology field schools throughout Canada. We also propose that the CSSS develop a national pedology certification and a web-based soil education resource. Also, further study is necessary to shed light on the contribution of non-disciplinary graduates to the professional practice and the impact this has on the perception of soil education in Canada.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".