Social perspectives on the use of reference conditions in restoration of fire‐adapted forest landscapes
Bibliographic record
Abstract
As approaches to ecological restoration become increasingly large scale and collaborative, there is a need to better understand social aspects of restoration and how they influence land management. In this article, we examine social perspectives that influence the determination of ecological reference conditions in restoration. Our analysis is based on in‐depth interviews with diverse stakeholders involved in collaborative restoration of fire‐adapted forest landscapes. We conducted interviews with 86 respondents from six forest collaboratives that are part of the U.S. Forest Service's Collaborative Forest Landscape Restoration Program. Collaboratives use a variety of approaches to develop reference conditions, including historic, contemporary, and future scenarios. Historical conditions prior to European settlement (nineteenth century or “pre‐settlement” conditions), or prior to more recent grazing, logging, and exclusion of fire, were the predominant type of reference used in all sites. Stakeholders described benefits and limitations of reference conditions. Primary benefits include (1) providing a science‐based framework for bringing stakeholders together around a common vision; (2) gaining social understanding and acceptance of the underlying need for restoration; and (3) serving to neutralize otherwise value‐laden discussions about multiple, sometimes competing, resource objectives. Limitations stem from (1) concerns over social conflict when reference conditions are perceived to contradict other stakeholder values and interests, (2) differing interpretations of reference condition science, (3) inappropriate application or over‐generalization of reference information, and (4) limited relevance of historical references for current and future conditions in some ecosystems. At the same time, collaboratives are adopting innovative strategies to address conceptual and methodological limitations of reference conditions.
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.027 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".