Comment on “Barriers to enhanced and integrated climate change adaptation and mitigation in Canadian forest management”
Bibliographic record
Abstract
We comment on the recent comprehensive review “Barriers to enhanced and integrated climate change adaptation and mitigation in Canadian forest management” by Williamson and Nelson (2017, Can. J. For. Res. 47: 1567–1576, doi: 10.1139/cjfr-2017-0252 ). They employ the popular barriers analysis approach and present a synthesis highlighting the numerous barriers facing Canadian forest managers. The underlying functionalist assumptions of such an approach are highly problematic from both a scholarly and a practical policy perspective. We argue that social scientists engaged in climate change research who want to influence policy-making should understand and then empirically apply causal mechanisms. Methods such as process tracing and qualitative comparative analysis (QCA) are promising tools that can be employed in national- or local-level assessments.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.040 | 0.034 |
| Insufficient payload (model declined to judge) | 0.017 | 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".