The Myths of Restoration Ecology
Bibliographic record
Abstract
From introduction:\n \n"Based on our experiences as researchers and practitioners in conservation and restoration ecology, we propose five central myths (Table 1) under which many ecological restoration and management projects seem to be conceived and implemented. Myths have value because they help us to organize and understand complex systems and phenomena. Identifying myths can help make the tacit explicit by revealing assumptions that are otherwise hidden. However, they remain simplified and potentially misguided models for understanding and application. The first Myth, the Carbon Copy, addresses the goal-setting process, and as such, it forms the basis of how restorations are evaluated. The Carbon Copy is closely tied to the remaining four myths, which involve the process of restoration and management: the Field of Dreams; Fast Forwarding; the Cookbook; and Command and Control: the Sisyphus Complex. We believe that describing these myths will be useful in understanding how some management or restoration strategies are conceived, designed, and implemented. For example, adherence to different myths may direct actions in divergent directions, as could be the case when choosing between a focus on ecosystem structure (Carbon Copy) or on key processes (Field of Dreams). Examining these myths may also help us better understand why some restoration projects do not meet our expectations. In the pages below, we briefly describe each myth and its assumptions, and give examples where the myth exists.\n \n"Our objective is not to abandon what we propose to be prevalent myths in ecological restoration--there are elements of truth in each--but to recognize that there are tacit assumptions associated with each myth. Failure to recognize these assumptions can lead to conflict and disappointing results despite large expenditures of time and effort. Our challenge is to recognize the limitations and not accept sometimes dogmatic beliefs without critical examination. We do not claim that every project is rooted in myth, but suggest that many perceived failures may be traced to over-reliance on one or more of the myths. We do not condemn restoration ecology, but rather provide a means of self-examination so readers can identify from their own experiences what worked and possible reasons for perceived failures."
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.078 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".