International interests: Student and industry perspectives on agicultural communications curriculum development in Ontario
Bibliographic record
Abstract
This study explores the potential development of an agricultural communications program at the [Institution]. It aims to understand the current knowledge of and interest in the discipline among Ontarian agriculture students and industry professionals, and the perceived importance and employability of hypothetical program graduates, contributing to the Sustainable Development Goal of quality education. Using a qualitative descriptive case study approach, focus groups with 18 students and six industry professionals were conducted. Data were collected through open-ended questions, analyzed using open coding and thematic analysis, and triangulated with demographic surveys. The findings reveal a general lack of understanding of agricultural communications among Ontarian students, who nonetheless recognize the field's potential to bridge gaps between producers and consumers, particularly through social media and diverse job opportunities. Industry professionals emphasized the growing importance of storytelling, crisis communication, and the need for poly-skilled graduates capable of addressing varied communication needs within the agricultural sector. Both stakeholder groups expressed interest in an agricultural communications academic program. Recommendations include engaging broader industry support for the program, integrating agricultural communications training across existing agricultural disciplines at the [Institution], and continuing research to refine curriculum development.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".