Training Environmental Managers to Control Invasive Plants: Acting to Close the Knowing–Doing Gap
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Many conservation land managers working with invasive plants rely largely on their own experience and advice from fellow managers for controlling weeds, and rarely take into consideration the scientific literature, a concrete example of a knowing–doing gap. We argue that invasion scientists should directly teach managers best practices for control. In 2013, we created a training program on five invasive plant species, specifically tailored to Québec (Canada) environmental managers. The course material was science-based, and included details on methods and costs. Here, we explain how this idea emerged, how the program was constructed and which types of managers were targeted. With modest resources, we reached 163 managers in less than 18 mo, who collectively oversee invasive species management for 41% of the Québec population. We presented factual information for all control methods, giving the environmental managers the tools to critically and objectively assess various options. Participants especially appreciated the highly practical content of the training and that they could submit their own invasion case for discussion. This program represents significant progress in narrowing the knowing–doing gap associated with the control of invasive plants in Québec, and we encourage such initiatives elsewhere for all fields of invasion biology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it