Current Situation and Key Influencing Factors of Sustainable Agricultural Talent Cultivation in Higher Vocational Colleges in Hunan Province
Bibliographic record
Abstract
This study examines the current state of sustainable agricultural talent cultivation in higher vocational colleges in Hunan Province and analyzes the relationships between key factors influencing educational outcomes: Student Development (STD), Teachers’ Effectiveness and Teaching Strategies (TEET), and School Capacity (SCC). A mixed-methods approach was used, combining literature analysis, questionnaires, and open-ended interviews. Participants included 380 students, 33 teachers, and 33 administrators selected through random sampling, with 12 participants purposively sampled for qualitative insights. Statistical analyses, including ANOVA, correlation, and regression, were conducted to assess perceptions and examine the interconnectedness of key factors. The findings reveal that sustainable agricultural talent cultivation efforts are perceived to be at a medium level overall. Strengths include teachers’ professionalism, collaborations with agricultural enterprises, and resource-sharing partnerships. However, challenges such as outdated curricula, insufficient funding, limited professional development opportunities, and inadequate practical training persist. ANOVA results showed significant differences in perceptions, with teachers and administrators rating efforts higher than students (p < 0.05). Regression analysis identified TEET as the strongest predictor of educational outcomes. Qualitative findings highlighted the need for curriculum updates, increased funding, and the establishment of stronger industry partnerships. Respondents also highlighted the transformative potential of technology to enhance educational quality, engage students, and prepare graduates for innovation. These results provide critical insights into the strengths and gaps in agricultural talent cultivation, offering practical implications for improving educational programs. Addressing these challenges and leveraging institutional strengths can better align educational efforts with industry demands, fostering sustainable agricultural development in Hunan Province and beyond.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".