Seasons in the Woods---Exploring Whether Seminar Attendance Is Influenced When Landowners Are Involved in Topic Selection
Bibliographic record
Abstract
Private woodland owners play a key role in maintaining and improving environmental amenities for local and downstream communities. To support these owners in making informed land-use decisions, extension professionals often rely on needs assessments to develop well attended educational outreach programs that reflect their ever changing educational needs. Recent work, however, has indicated a low correlation between woodland owners' expressed educational needs and seminar attendance.;Seasons in the Woods is a three-part woodland focused education series used to explore whether landowners would be more likely to express interest, register, and attend forestry education seminars if they were given the chance to select the seminar topic. Outreach to 3600 woodland owners was conducted exclusively by direct mail. To control for variation in responses due to general public interest in educational seminars and to facilitate the monitoring of workshop participants. Seminars were delivered approximately four months apart.;Our study showed a low, non-significant correlation between expressed interest in topics and actual attendance. The treatment group whom was able to select the topic showed a greater attendance rate (1.11%) than those who only received a postcard invitation (0.69%), but a slightly smaller attendance rate than the group who were just invited to the 3-part series (1.21%). The surprising finding was that almost half (42%) of the attendees to seminar one were New Comers (NC). The New Comers were participants who attended the first seminar, even though they received zero direct mail contact or invitations from this study. Once the NC's were assigned to the corresponding treatment group of the person that they attended with, the numbers of attendees among treatment groups changed so much so that there was a significant association between treatment and seminar attendance.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".