A Survey of North American Horticulture Graduate Programs: Demographics, Policies, Finances, and Metrics
Bibliographic record
Abstract
A comprehensive survey of American and Canadian universities that offer masters, doctoral, or both degrees in horticulture resulted in responses from 27 academic units. Units were surveyed regarding types of degrees offered, admissions policies, demographic characteristics of students, financial assistance provided to students, faculty ranks and salaries, and metrics by which the programs were evaluated by university administration. About 80% of the programs resided in 1862 Morrill Act land-grant institutions (LG) with the remainder housed in other non-land-grant institutions (NLG). Thirty-eight percent of reporting LG programs existed as stand-alone horticulture departments, whereas horticulture programs were combined with other disciplines in the remainder. Admissions criteria were most consistent among LG programs. Participation in distance education programs was low, but growing. Financial support of graduate students was more common in LG programs. Most schools offered some sort of tuition reduction to those students on assistantships/fellowships and offered health insurance options. Payment of fees was rare and the level of stipends provided varied substantially among programs. International student enrollment was greatest at LG programs and had remained steady in recent years. Gender equity was present among graduate students, with nearly equal male and female enrollment. Most graduate students at both LG (63.6%) and NLG (75.0%) programs were non-Hispanic White; although overall minority enrollment had increased but was still not similar in distribution to that of the general U.S. population. Professors (46.7%) and Associate Professors (28.3%) dominated the faculty ranks while Assistant Professors (19.3%) and lecturers/instructors (5.7%) constituted a much smaller portion of the faculty. Faculty salaries varied tremendously among institutions, especially for senior faculty. Female and ethnic minorities were underrepresented in faculty ranks compared with the general U.S. population. Aside from total graduate program enrollment, the relative importance of various evaluation metrics for programs was highly variable among institutions. Data discussed herein should be useful to universities with horticulture graduate programs for peer institution comparisons during program assessments, accreditation reviews, or for strategic planning purposes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".