Linking gender, climate change, adaptive capacity, and forest-based communities in Canada
Bibliographic record
Abstract
Analyses of climate change and the forest sector have identified the importance of individual actors, institutions, and organizations within communities for effective adaption and climate mitigation. Yet, there remains little recognition of how the internal dynamics of these institutions and organizations are influenced by gender and other social considerations such as age and culture. Research from developing countries and cognate resource sectors suggests that these considerations are critical for enhancing local adaptive capacity. Despite extensive review of forestry research across North America and western Europe, we found almost no research that addresses how differential social capabilities within forest-based communities affect adaptation to climate change. In this paper, we document the potential that gender sensitivity might provide to conceptions and practical applications of adaptive capacity and identify four types of research opportunities to address this gap: (i) developing disaggregated capitals frameworks; (ii) creating inclusive models; (iii) informing social planning; and (iv) understanding gender mainstreaming. Research focused on these opportunities, among others, will provide more robust theoretical understanding of adaptive capacity and strategic interventions necessary for effective adaptation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".