Theorizing the Implications of Gender Order for Sustainable Forest Management
Bibliographic record
Abstract
Sustainable forest management is intended to draw attention to social, economic, and ecological dimensions. The social dimension, in particular, is intended to advance the effectiveness of institutions in accurately reflecting social values. Research demonstrates that while women bring distinctive interests and values to forest management issues, their nominal and effective participation is restricted by a gender order that marginalizes their interests and potential contributions. The purpose of this paper is to explain how gender order affects the attainment of sustainable forest management. We develop a theoretical discussion to explain how women's involvement in three different models for engagement—expert-based, stakeholder-based, and civic engagement—might be advanced or constrained. By conducting a meta-analysis of previous research conducted in Canada and internationally, we show how, in all three models, both nominal and effective participation of women is constrained by several factors including rules of entry, divisions of labour, social norms and perceptions and rules of practice, personal endowments and attributes, as well as organizational cultures. Regardless of the model for engagement, these factors are part of a masculine gender order that prevails in forestry and restricts opportunities for inclusive and sustainable forest management.
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".