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Record W2530677216 · doi:10.1139/cjss-2016-0058

Profiling Undergraduate Soil Science Education in Canada: Status and Projected Trends

2016· article· en· W2530677216 on OpenAlexaffvenueabout
Amanda Diochon, Nathan Basiliko, Maja Kržić, Thomas Yates, Edith Olson, Jacynthe Masse, B. D. Amiro, Darshani Kumaragamage

Bibliographic record

VenueCanadian Journal of Soil Science · 2016
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of WinnipegUniversity of ManitobaLaurentian UniversityUniversity of SaskatchewanLethbridge CollegeUniversity of British ColumbiaLakehead University
Fundersnot available
KeywordsAgricultureScience educationHigher educationPolitical scienceGeographySociologyPedagogyArchaeology

Abstract

fetched live from OpenAlex

Global declines in postsecondary enrollment in soil science programs over the last several decades have been mainly attributed to an overemphasis on the connection with agronomy and production agriculture but recent enrollment increases in the USA suggest change is afoot. To determine if similar trends are occurring in Canada, we inventoried undergraduate soil science course offerings at postsecondary institutions and conducted a survey to assess the status and projected trends in soil science education. We found that 64% of universities and 37% of colleges offer undergraduate soil science courses as part of degrees or diplomas in which knowledge of soil science is important (e.g., agriculture and resource management). In Canada, there are 149 undergraduate soil science courses taught in universities and 58 at colleges. On average, there are 3.2 courses taught at each university and 1.9 at each college that offer soil science courses. Soil science programs at the University of British Columbia, University of Saskatchewan, and University of Manitoba offer between eight and nine soil science courses and represent 17.4% of the national total. Enrollments in all courses across the country are projected to be steady with some anticipated growth, trends that are consistent with those reported in the USA and the Netherlands.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.209
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2016
Admission routes3
Has abstractyes

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