The evolution of Society for Ecological Restoration's principles and standards—counter‐response to Gann et al.
Bibliographic record
Abstract
In response to our recent article (Higgs et al. 2018) in these pages, George Gann and his coauthors defended the Society for Ecological Restoration (SER) International Standards, clarified several points, and introduced some new perspectives. We offer this counter‐response to address some of these perspectives. More than anything, our aims are in sharpening the field of restoration in a time of rapid scaling‐up of interest and effort, and support further constructive dialogue going forward. Our perspective remains that there is an important distinction needed between “Standards” and “Principles” that is largely unheeded by Gann et al. (2018). We encourage SER to consider in future iterations of its senior policy document to lean on principles first, and then to issue advice on standards that meet the needs of diverse conditions and social, economic, and political realities.
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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 | 0.102 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.028 | 0.046 |
| Insufficient payload (model declined to judge) | 0.003 | 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".