Understanding the diversity of objectives among women forest owners in Finland
Bibliographic record
Abstract
Women forest owners control a significant share of private forests in Finland, but they are often underrepresented in forest-related surveys. It also seems that women forest owners are less active in performing forestry work or selling wood; however, the information on women forest owners is sparce. The purpose of this study is to deepen understanding of the association between women forest owners’ activities and their ownership objectives. The study analysed a large survey ( n = 6468) collected as part of the Finnish Forest Owner 2020 research project. Overall, women forest owners were found to be less active in many aspects compared to their men counterparts. With k-means clustering, four different clusters of women forest owners with different ownership-objective profiles were identified. Differences in ownership activities between the clusters were compared using Pearson's chi-square test. Multi-objective women forest owners were more active than other women owners. Moreover, women forest owners who valued nature and recreation were less active than other women forest owners. This might indicate the lack of service-dominant logic in forestry-related services. By providing a more detailed understanding of women forest owners, the results aid the design of more equal and inclusive forest policies and forest services.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".