Bibliographic record
Abstract
Agritourism is an effective way to promote sustainable agricultural practices and agricultural literacy. As such, agritourism is an increasingly important way for the general public to learn about agricultural practices, issues, and concepts. It is commonly assumed that agritourism experiences result in learning, yet there is very little research that demonstrates this or explores what kind of learning is possible. This research used personal meaning maps to understand visitors' free-choice learning in different agritourism contexts. The data was analyzed using a mixed-methods approach, where the qualitative data provided insight into the categories of agricultural learning, and the quantitative data demonstrated how much learning occurred in relation to extent, breadth, depth, and mastery of learning. This research found that all forms of agritourism broadly supported visitors’ free-choice learning but occurred primarily in relation to breadth and extent, rather than depth and mastery of learning. Supporting prior research in agritourism and learning, these results demonstrate the effectiveness of all forms of agritourism in facilitating meaningful learning but that many of these learning opportunities remain poorly planned for. Deeper and more complex forms of learning are possible when intentionally linking agritourism experiences to agricultural literacy goals.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".