Climate change adaptation and sustainable forest management: A proposed reflexive research agenda
Bibliographic record
Abstract
This article is a synthesis of the salient topics discussed in the Climate Change Adaptation and Sustainable Forest Management (SFM) Workshop, held at the University of British Columbia, February 14–16, 2011, and lays out a research agenda based on the recommendations for future research that emerged in the workshop. The proposed research agenda is framed using the theory of reflexive modernization to enable the forest research community to consider how different modes of knowledge production can support adaptation within SFM. The workshop discussions highlighted the importance of considering extreme events and high uncertainty in planning for adaptation within SFM. Participants discussed the utility of climate change modeling and risk assessment for local decision-making. In addition, there was general agreement that adaptive collaborative management could facilitate adaptation within SFM, despite difficulties in implementation. The recommendations for future research that emerged from the workshop focused on climate change-related assessments, modeling techniques, and governance and institutional enablers/barriers to adaptation. This broad research agenda, however, can be approached using different modes of knowledge production, illustrating different orders of reflexivity. Apart from a call for more traditional academic research to improve SFM under climate change, workshop participants referred to the need for participatory research, in which researchers would be embedded in communities and other contexts of application, engaging in a “client-focused” partnership model to produce knowledge that is robust, compelling, legitimate and, above all, locally relevant. It is hoped that this alternative mode of knowledge production would result in a social license and greater political will to accelerate adaptation within SFM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.212 | 0.083 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.012 | 0.058 |
| Scholarly communication | 0.042 | 0.047 |
| Open science | 0.010 | 0.027 |
| Research integrity | 0.026 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 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".