Student responses to Rapson (2023) and the pedagogical value of opinion articles in restoration ecology
Bibliographic record
Abstract
In 2023, Rapson introduced the term “tertiary succession” to describe the recovery trajectories of ecosystems that have experienced human intervention (i.e. restoration). We assigned Rapson (2023) as seminar material in a fourth‐year undergraduate class to reinforce key concepts (primary succession, secondary succession, recovery trajectories, and timelines) and support discussion‐based learning. Following the seminar, we asked students to explain whether they thought tertiary succession, as proposed, was a useful term for restoration. Student opinions were relatively even: 38% of students agreed, 47% disagreed, and 17% were mixed. Of those that did not find utility in the term, 53% felt that the term was redundant or unnecessary. Another 24% believed that the term ignored key aspects of ecological restoration, like cultural or social objectives, and ignored recovery trajectories that do not follow successional pathways. Of those that did find utility in the term, 37% believed the term would help differentiate natural successional processes from assisted recovery. We believe opinion articles are powerful pedagogical tools that can be used in ecological education to reinforce key concepts and theories, support critical thinking, and foster logical decision‐making.
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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 | 0.031 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".