Cultivating resilience: women's participation as a catalyst for resilience and sustainability in the Canadian Forest Sector
Bibliographic record
Abstract
In the men-dominated landscape of the Canadian Forest Sector (CFSec), gender balance and equity emerge as a potentially pivotal force for transformation and sustainability. This thesis explores the complex interplay between gender and sustainability in the CFSec, with a focus on strategies to enhance the recruitment, retention, and gender balance. This research contributes to developing a more inclusive, innovative, and resilient forest sector in Canada through an analysis of current practices, perceptions, value attribution, and potential solutions. The study employs a transdisciplinary sustainability framework, combining insights from multiple bodies of knowledge and collaborating with non-academic partners, utilizing a mixed-methods research design. This thesis has three standalone and interconnected studies: (1) a systematic literature review examining the state of gender balance in the CFSec over the past decade; (2) an exploration of strategies for building a resilient and gender-balanced workforce according to key stakeholders (e.g., governmental organizations, industry and workforce associations, training institutes) across Canada and the United States, with a focus on Newfoundland and Labrador (NL); and (3) an examination of the perceptions, value attributions, and attitudes of diverse demographic groups in NL towards forests, the forest sector, and efforts to increase women's participation in the industry. Findings reveal persistent underrepresentation of women in the sector, with women comprising only 16.4% of the CFSec workforce (2021) and reportedly much lower in certain roles and regions, such as NL. Women face significant barriers, including discrimination, harassment, and a lack of or unsatisfactory work-life balance and career options and progression. Findings revealed that female participants attributed value to a broader range of forest assets without undervaluing the sector's economic aspects. This research argues that improving gender balance and gender equity in the sector can be a strategic move to enhance its innovation and adaptability, particularly as the industry transitions towards a circular bioeconomy. Nevertheless, the study also found that male participants, forest sector workers, and those in rural areas were more resistant to including women in the sector, particularly in decision-making positions. The thesis concludes with action-oriented recommendations for various stakeholder groups aimed at fostering debate and guiding practical steps towards including and retaining more women in the sector, as well as improving its resilience and sustainability.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".