Reply to the comment by Wellstead et al. on “Barriers to enhanced and integrated climate change adaptation and mitigation in Canadian forest management”
Bibliographic record
Abstract
In commenting on our review about barriers to climate change adaptation and mitigation (Williamson and Nelson 2017, Can. J. For Res. 47(12): 1567–1576, doi: 10.1139/cjfr-2017-0252 ), Wellstead et al. (2018, Can. J. For. Res. 48(10), doi: 10.1139/cjfr-2017-0465 ) argue that the “functionalist assumptions” underlying barriers analysis in general and our paper in particular are problematic. They also argue that barriers analysis — a method widely employed both in scholarly climate change adaptation research and in national and international climate change assessments — should be replaced by approaches that remain untested in the context of climate change adaptation research, particularly in forestry adaptation and mitigation research. We believe that neither the scholarly research on adaptation and mitigation barriers nor our review have characteristics of functionalism or imply functionalist assumptions. Moreover, we disagree that barriers analysis can be replaced by the methodologies that they propose because these latter approaches address different aspects and features of processes supporting movement toward comprehensive and integrated adaptation and mitigation in forest management. We do agree that there are knowledge gaps relative to examinations and explanations of causal mechanisms to explain decision-making and policy process outcomes that have already occurred, and we encourage research to address these gaps. Ultimately, this aspect of social science research is complementary to, not a substitute for, barriers research.
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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 | 0.019 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.043 | 0.050 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".