Training Environmental Managers to Control Invasive Plants: Acting to Close the Knowing–Doing Gap
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.
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".