Opportunities for Academic Training in the Science and Practice of Restoration within the United States and Canada
Bibliographic record
Abstract
Abstract With increasing interest in ecosystem restoration, there is a corresponding need for trained professionals who understand not only the science of restoration ecology but also the management practices and social factors that lead to successful project implementation. We surveyed 300 academic institutions in the United States and Canada with research programs in the ecological, conservation, and natural resource sciences to determine whether current curricula in restoration meet the needs of this growing field. Opportunities to obtain a degree specifically in the field of restoration were extremely limited: only 11 institutions (4%) offered undergraduate degrees, and only four (1%) offered graduate degrees. Half of these programs were established within the last 5 years. Only three programs required more than one course in restoration, and over half did not include any specific core courses in the social sciences. There were more opportunities to obtain a degree relevant to restoration but not specifically restoration focused: 21% of institutions offered such undergraduate degrees and 12% offered graduate degrees. With regard to courses, only 23% of institutions offered courses that specifically focused on restoration, whereas 43% offered courses that included “restoration” in their description. These trends suggest that although training opportunities are increasing, the current pool of programs and courses that explicitly focus on restoration is not sufficient to meet current demand for qualified and experienced restorationists or to ensure that these scientists and practitioners have the training necessary to meet the complex challenges of restoring degraded ecosystems in the future.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".